Effective Prediction Model for Heart Disease Using Machine Learning Algorithm


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Effective Prediction Model for Heart Disease Using Machine Learning Algorithm

G. Nagrajan1

1-Assistant Professor

Department Of Computer Science and Engineering

K.S.R. College of Engineering

A.P Mohan Raju2,V. Logeshwaran3,

K. Nandhakumar4,S. Naveenkumar5

2345-UG Students

Department Of Computer Science and Engineering

      1. College of Engineering

        Abstract Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures from large amounts of data stored in databases and data warehouses. The most basic definition of data mining is the analysis of large data sets to discover patterns and use those patterns to forecast or predict the likelihood of future events. Data Mining is one of the most critical aspects of automated disease diagnosis and disease prediction. It involves developing data mining algorithms and techniques to analyze medical data. In present, heart disease has excessively increased and heart diseases are becoming one of the most fatal diseases in several countries. In this paper, heart patient datasets are investigate for building classification models in order to predict heart diagnosis.

        This paper implements feature model construction and comparative analysis for improving prediction accuracy of heart disease in three phases. In first phase, normalization algorithms are applied on the heart patient datasets collected from UCI repository. In second phase, by the use of HCR-PSO (Highly Co- Related Attribute Practical Swarm Optimization) feature selection, using a subset (data) of heart patient from whole normalized heart patient datasets is obtained which comprises only significant attributes and then applying selected classification algorithms on obtained, significant subset of attributes.Third phase, classification algorithm KNN, Random forest, J48, SVM, Bayesian network and MLP algorithm is considered as the better performance algorithm, because it gives higher accuracy in respective to other classification analytical Model before applying HCR-PSO feature selection. But, J48 algorithm is considered as the better performance algorithm after applying HCR-PSO feature selection. In third phase, the results of classification algorithms with and MAE and RMSE validation metric are compared with each other. The results obtained from our experiments indicate that J48 algorithm outperformed all other techniques with the help of feature selection with an accuracy of 94.40%.

        .

        Keywords Mobility, load balancing, MCC, cyber foraging

        1. INTRODUCTION

          A. BIG DATA MINING

          Big data defines as large volumes of high velocity, complex, and shifting data that require advanced skills and technologies to set up the capture, storage, distribution, management and analysis of the information. Big data enclose such characteristics as variety, velocity and, with respect especially to healthcare, integrity. Current analytical techniques can be applied to the large amount of existing patient related health and medical data

          to reach lower understanding of results, which then can be applied at the point of care. Ideally, unique and population data would inform each specialist and her /his patient while the decision-making process and help Regulate the most proper treatment option for that particular patient.

            1. IG DATA HEALTH CARE DATA

              Big data in healthcare refers to electronic health data sets so large and complex that it is difficult to manage with traditional or common data management methods and traditional software and/or hardware. Some health care data are characterized by a need for timeliness; for example, data generated by wearable or implantable biometric sensors; blood pressure, or heart rate is often required to be collected and analyzed in real-time. Data in healthcare can be categorized as follows

              • Clinical Data and Clinical Notes About 80% of this type data are unstructured documents, images and clinical or transcribed process.

              • Structured data (e.g., laboratory data, structured EMR/HER)

              • Unstructured data (e.g., post-op notes, diagnostic testing reports, patient discharge summaries, unstructured EMR/HER and medical images such as radiological images and X-ray images)

              • Semi-structured data (e.g., copy-paste from other structure source)

          1. DATA MINING

            Data mining is that the method of realizing models in giant facts sets involving tactics at the fork of machine learning and systems. Its an important method wherever intelligent strategies are applied to extract knowledge pattern. The information mining is also accomplished exploitation classification, clustering, prediction, association and statistic analysis. Data processing is that the study of huge datasets to haul out hidden and antecedently unknown models, relationships and information to facilitate are intricate to realize with ancient applied mathematics tactic. Therefore data processing refers to mining or extracting information from giant amounts of facts.

          2. TIME SERIES DATA

          Statistic information is obtained at determined quantity from any system. Daily value modification of a market, current associated voltage information of an induction motor and therefore the population distribution consistent with year in a very state can moreover live thought of as a statistic. Such a statistic contains events of interest. One introduce approach is to spot essential, time- ordered structures, known as temporal pattern, that are it hidden in equivalent weight and are quality of fascinating events. Statistic analysis is to an interest in predicting future values from diversion series information. A statistic example has been given in equation (1).

          X= xi, ———–xn 1

          In such a statistic, t is time index and N is that the total variety of observations. Necessary events area unit fashioned over the time.

        2. RELATED WORKS

          J.Vijayashree et al [1] describes a heart cardiopathy and main sources of end round the world and it's imperative to predict the disease at a premature part. The computer aided systems facilitate the doctor as a tool for predicting and identification cardiopathy. The target of this review is to widespread regarding Heart connected upset and to temporary regarding existing call support systems for the prediction and diagnosing of cardiopathy supported by data processing and hybrid intelligent techniques .

          Dilip Roy Chowdhury [2] represents the employment of artificial neural networks in predicting infant unhealthiness identification. The projected technique involves training a Multi Layer Perceptron with a BP learning rule to acknowledge a pattern for the designation and prediction of infant diseases. The rear propagation rule was

          Accustomed train the ANN styles and conjointly a similar has been tested for the assorted categories of infant unhealthiness. Regarding ninety four cases of assorted sign and symptoms, parameter is tested during this model. This study exhibits ANN primarily based prediction of infant unhealthiness and improves the identification accuracy of 75 with higher stability. Niti Guru et al [3] planned a system that uses a neural network for prediction of upset, pressure level, and sugar. a set of cardinal records with 13 attributes area unit used for coaching and testing. He urged supervised network for identification of upset and trained it exploitation back propagation formula. On the concept of unknown data is entered by a doctor the system ca n noticethat unknown data from coaching information and generate a listing of attainable unhealthiness from that patient can suffer.

          Ms. Ishtake S.H et al [4] was enforced a model cardiovascular disease prediction system is developed victimization three processing classification modeling techniques specifically, Decision. Trees, Naïve Bayes and Neural Network The system extract hidden data from historical cardiovascular disease data. DMX query language and functions are accustomed build and access the models. Five mining goals are outlined supported business intelligence and data exploration. The goals are evaluated against the trained models. All three models would possibly answer difficult queries, every with its own strength with relevance simple model interpretation, access to elaborate information and accuracy.

          Mohammad Taha Khan et al [5] bestowed image model for the carcinoma additionally to upset prediction using processing techniques. Two decision tree algorithms C4.5 and additionally the C5.0 is employed on these datasets for prediction and performance of every algorithmic rule ar compared. Pruning algorithmic program is use d to reduce a slip-up and avoiding the over-fitting. Pruning a tree is that the action to interchange a whole subtree by a leaf. The replacement takes place if the expected error rate within the subtree is bigger than within the single leaf. throughout this study, they started by generating the whole (generally over fitted) classification tree and alter it using pruning merely once.

          Sang Hun Han et al [6] designed a framework to gather and store numerous domains of information on the causes of upset, and created a giant information integrated information. a spread of open supply databases were integrated and migrated onto distributed storage devices. The integrated information was composed of clinical information on vessel diseases, national health and nutrition examination surveys, applied math geographic data, population and housing censuses, meteorologic administration information, and insurance Review and Assessment Service information. The framework was

          composed of information, speed, analysis, and repair layers, all hold on on distributed storage devices. Finally, we tend to planned a framework for a upset prediction system supported lambda design to resolve the issues related to the period of time analyses of massive information. this method will be wont to facilitate predict and diagnose diseases, like cardiovascular diseases.

          Kiran et al [7] describe Lambda design and an enormous knowledge technique which will be accustomed support period analyses but, it's the limitation of not having the ability to investigate an outsized volume of knowledge in real time. To deal with this limitation, a way may be utilized that blends knowledge created before in an exceedingly batch layer with knowledge processed in real time. Then, the information may be generated and keep. to attain this, knowledge area unit shaped in batch read in an exceedingly cycle with a batch layer, and identical knowledge area unit shaped in period read via period processing. These two knowledge sets area unit then alloyed and analyzed, facultative the analysis of information that reflects period data. Supporting this, Amazon net Services, that process massive knowledge, wrote a white book on the mixing of execution and data processing into one network exploitation lambda design.

          Cheryl Ann Alexander associate degreed Lidong Wang et al [8] is describe an Acute infarct (heart attack) deadliest diseases patients face and therefore the key to disorder management is to guage massive legion datasets, compare and mine for data which will be accustomed predict, prevent, manage and treat chronic diseases like heart attacks. massive knowledge analytics, acknowledged within the company world for its valuable use in dominant, contrastive and managing massive datasets may be applied with abundant success to the prediction, prevention, management and treatment of disorder. data processing, visual image and Hadoop area unit technologies or tools of massive knowledge in mining the voluminous datasets for data. The aim of this literature review was to spot usage of massive knowledge analytics in coronary failure prediction and bar, the employment of technologies applicable to massive knowledge, privacy issues for the patient, and challenges and future trends similarly as suggestions for additional use of those technologies. The results can guide suppliers, tending organizations, nurses, and different treatment suppliers in exploitation massive knowledge technologies to predict and manage coronary failure and tailored medical treatment may be developed exploitation these technologies

          Sakthi Sakunthala,N, S.[9] introduces the characteristics of massive knowledge, health care knowledge and a few major problems with massive knowledge. massive knowledge in health care is employed to predict the diseases, analyze the symptoms, imp rove the identification, offer drugs properly for the patients to pass though heart diseases, and enhance the standard of care , lower the value, improve the era and to cut back the impact of death in advance. There is several organizations and university have joined along to produce a solutions for bismuth g knowledge in health care. These problems embody massive knowledge advantages, its applications and opportunities in Medicare and health care. These days many of us within the world area unit laid low with heart connected diseases. Massive knowledge plays a significant role so as to save lots of the patients health and to cut back the death of heart patients. Apollo Hospital and therefore the US – primarily based Alive cor opposition collaborated along to create the Mobile ECG 4 (Electro Cardio Gram) that monitor the stroke and heart disease (irregular Heart Beat) screening through mobile devices. The sensors that area unit mounted on the mobile devices monitors the patients heart beat by merely rest it on their chest. The patient health data is automatic ally recorded

          through mobile devices within the style of ECG and so its directly uploaded to the patients knowledge bases

          Saranya P [10] describes a Clinical knowledge relation the phenotypes associate degreed dealing of patients denotes an underused knowledge font that has abundant huge knowledge analysis doubtless than is presently grasped. Mining of electrical health records has the power to make a brand new patient- stratification doctrines and for tight fitting unknown sickness links. Commixture EHR knowledge with genetic knowledge will provides a lot of quite genotype-phenotype affairs. However, a good series of allowable, ethic al and method reasons presently be patient the organized confession of that knowledge in electrical health histories and their excavating. Here, it contemplate the possible for furthering examination and experimental care mistreatment EHR knowledge and therefore the tasks that has to be dazed before this is a truth.

          Vinitha S [11] describes to huge knowledge progress in medical specialty and health ca re communities, correct study of medical knowledge advantages early sickness recognition, patient care and community services. Once the standard of medical knowledge is incomplete the exactitude of study is reduced. Moreover, totally different regions exhibit distinctive appearances of sure regional diseases, which can leads to weakening the prediction of sickness outbreaks. Within the projected system, it provides machine learning algorithms for effective prediction of varied disease occurrences in disease-frequent societies. It experiment the altered estimate models over real-life hospital knowledge collected. to beat the problem of incomplete knowledge, it uses a latent issue model to build the missing knowledge. It experiment on a regional chronic eudemonia of cerebral infarct. Mistreatment structured and unstructured knowledge from hospital it uses Machine Learning call Tree formula and Map cut back formula.

          Kelvin KF Tsoi1 et al [12] describes thestudy on call trees and their behavior in inward to the conclusion. Tree node cacophonic supported relevant feature choice may be a key step of call tree learning, at constant nowadays their major shortcoming: the algorithmic nodes partitioning ends up in geometric reduction of information amount within the leaf nodes that causes associate degree excessive model complexness and knowledge over fitting. During this paper, the author bestowed a unique design referred to as a choice Stream.

        3. SYSTEM DESIGN

          1. OBJECTIVEOF RESEARCH

            • Classification prediction model to just about all of the foremost extremely cited distance measures within the connected on heart condition datasets.

            • Nearest Neighbor classifiers are the most effective method and take into account alternative classifiers, as well as neural networks and J.48 algorithm.

            • To improve analysis study area by increasing our search area to incorporate deletion.

            • Neural network approach is captures the optimization values and uses these values to represent the statistic measurement.

            • PSO optimization selection model to detect the key modification points in anytime series, and uses these points to represent the whole statistic.

            • NN classification approach is to interrupt a variable statistic instance into multiple univariate-time series data then every time series is processed individually into disjoint segments and also the aggregate distance is generated.

          2. PROBLEM DEFINITION

            Big Data is based on data obtained from the whole process of diagnosis and treatment of each case. Big Data analytics can perform predictive modeling to determine which patients are most likely to benefit from a care management plan. Big Data offers a lot of benefits such as heart disease prevention, reduced medical errors and the right care at the right time and better medical outcomes. In addition, Big Data can improve the Research and Development (R&D) and translation of new therapies.

            Big data has great potential to improve medicine, guide clinicians in delivering value-based care. Big Data has challenges include consolidating and processing segmented or siloed data, aggregating and analyzing unstructured data, indexing and processing continuously streaming data and unified standards fort heart dataset. These future research works can be: aggregating and analyzing unstructured heart dieses data set, indexing and processing of continuously stream data set using NN classification algorithm.

            The proposed technique is producing an enhanced concept over the heart disease prediction within novel data mining techniques NN Classification with PSO feature selection and the fuzzy classifier. The HRC-PSO is an enhanced approach for classification with optimization association rule with Support and Confidence Framework to extract Association rule from big data warehouse. The neural network classification is the technique to classify the attributes from the patient record. The neural network classification and PSO optimization with association classifier can enhance the classification performance and accuracy of the heart disease diagnosis. These all metrics is being verified by the experts and professional doctors of heart specialist.

          3. EXISTING WORK

            The main purpose of feature selection process is to find the minimum number of relevant attributes from a given heart dataset described by 14 attributes (features). Most real life problems need an optimal and acceptable solution rather than calculating them precisely at the cost of degraded performance, time and space complexities. Therefore, it is necessary to carry out the analysis using Greedy selected features.

            The Greedy feature subset can be used to reduce the number of attributes used in classifiers by maintaining the classification accuracy. Less discriminatory features are eliminated, leaving a subset of the original features which retains sufficient information to discriminate well among classes. Greedy Feature extraction is a more general method in which the original set of features is transformed to provide a new set of features.

            In data analysis and classification process, feature selection is an important step to avoid performance degradation in data classification, rule extraction and building decision support systems. Greedy Feature selection processes the learning algorithms of supervised and unsupervised in terms of speed and predictive accuracy. Irrelevant and redundant features interfere with useful ones, so that most supervised learning algorithms fail to properly identify those features that are necessary to describe the target attribute. By enabling generalization algorithms to retrieve the most suitable feature subset, significantly increases the probability of obtaining simpler, more understandable and predictive models of the data.

          4. PROPOSED WORK

          PSO algorithm uses a population of randomly generated particles having associated velocity and position, where each particle corresponds to randomly generated solution. PSO searches for optimal solution by iteratively changing the heart beat Rate

          and attack disorder experience of own particle and of group toward gbest and pbest location in successive iteration. gbest corresponds to best fitness value of population that any heart data set particle has achieved while pbest corresponds to best fitness value of particle that it has achieved so far.

          Feature selection method based on PSO and rough set theory as imprecision, uncertainty can be easily tackled by rough set theory. Experimental results show that PSO is a good choice for rough set based feature selection. PSO can easily get stuck in local optimum solution after several generations. Therefore, several strategies have been adopted to improve the performance of PSO.

          PSO based feature selection algorithm has been used in this work in order to select suitable subset of features so that they are potentially useful in classification. Another advantage of PSO based feature selection in this work is that it finds and eliminates the redundant features if any because these redundant features may misguide in clustering or classification. The reduction in number of features reduces the training time and ambiguousness.

          Therefore, in this paper work, an approximated inconsistency measure genetic feature selection algorithm based upon filter model has been proposed which has substantially reduced the search space and increased global search capability and is better in attribute interaction when compared to other algorithm like greedy method. The PSO algorithm proposed for feature selection uses the standard optimal operator where the fitness of individuals is computed using the inconsistency count measure.

          ADVANTAGES

          • PSO algorithm test the effectiveness of inconsistency measure and inconsistency factor as a criterion to evaluate fitness of feature time series subsets obtained

          • It is evaluate the accuracy of classifiers with respect to the selected time series feature subset.

          • It is evaluate the performance of the algorithm in terms of CPU time needed to reach a good solution.

          • The specified size of the time series subset of features that optimizes an evaluation measure

          • The smaller size of the time series subset that satisfies a certain restriction on evaluation measures

          • In general, the subset with the best commitment among size and evaluation measure

        4. SYSTEM METHODOLOY

          1. BIG DATA ANALYTICAL MODEL

            Big data analytics support the concept of artificial intelligence and lie at the heart of many new digital health platforms and precision health tools. Ideally, utilization of big data analytic tools in cardiovascular care will translate into better care and outcomes at a lower cost.

            The potential for more powerful predictive models is an appealing application of big data analytics. Historically, prediction models have relied on a limited number of specified variables manually entered to estimate a risk score. Such models generally lack precision: they perform reasonably well at the population level, but not at the individual patient level. And despite the existence of dozens of risk models related to cardiovascular conditions, few are utilized to make therapeutic decisions.

            Big data analytics may yield more powerful prediction of outcomes ranging from mortality to patient-reported outcomes to resource utilization, and thus could be more clinically actionable. Machine learning, for example, evaluates patterns associated with an outcome directly from the data, rather than from a pre-specified set of variables. A full range of associations and interactions among the data are assessed. Whereas traditional statistical models are one and done, machine learning uses a training process whereby the model is iteratively given varied data sets to explore many combinations of predictive features to optimize prediction.

            Phenol-mapping, or deep phenol-typing, is another promising big data application. Current disease classifications, or phenotypes, are imprecise and heterogeneous Big data analytics can identify similar patient clusters, creating multiple phenotypes within each disease entity. In theory, more refined phenol-mapping of disease states and trajectories should help inform more tailored- health decisions

            Big data methods can support the combination of multiple data sources from large patient populations to better estimate the potential benefits of therapies such as ICDs for individual patients. Indeed, big data analytic methods are central to the success of precision health, given the growing interest in incorporating -omic data, which vastly increases the size and complexity of datasets. Such datasets require advanced analytic platforms and methods that are the hallmarks of big data analytics.

            Big data analysis can guide policies to address a certain patient segment by specific interventions. The success of the policy is critically dependent on the quality of the underlying research and the quality (effectiveness) of the interventions. For many interventions (for instance in the social/mental health domain) universally accepted methods for validating success are still lacking. There is several challenges solution regarding Big Data and population heart disease such as:

            • Data protection regulation makes it difficult to analyze data from different heart disease providers and services in combination

            • A significant part of the population health records is unstructured heart disease text

            • There are interoperability, data quality and data integration limitations

              Data Set Collection

              (Heat Dataset) UCI- Repository

              Convert Time Stream Data [Heart Beat Rate]

              Min/ Max Normalization Process

              Feature Selection Algorithm

              Greedy Feature Extraction PSO Feature Extraction

          2. NORMALIZATION MODEL

            Data transformation such as Normalization is a data preprocessing tool used in data mining system. An attribute of a dataset is normalized by scaling its values so that they fall within a small-specified range, such as 0.0 to 1.0. Normalization is particularly useful for classification algorithms involving neural networks, or distance measurements such as nearest neighbor classification and clustering. There are many methods for data normalization includes min-max normalization, z-score normalization and normalization by decimal scaling.

            Min-max normalization performs a linear transformation on the original heart dataset. Min-max normalization maps a value d of P to d in the range [new_min(p), new_max(p)].

            Min max normalization preserves the relationship among the original heart dataset values. The table 4.1 Describe a sample normalized heat disease dataset model details shows,

            Attribute

            Original Values

            Normalized dataset

            age

            70.0

            50.0

            chest pain type

            1.0

            0.0

            resting blood pressure

            130.0

            150.0

            maximum heart rate achieved

            109.0

            78.0

            exercise induced angina

            0.0

            1.0

            Table 4.1 Normalized Heart Dataset

          3. GREEDY FEATURE EXTRACTION MODEL

            Feature selection is one of the dimension reduction techniques which have been used to allow a better understanding of data and improve the performance of other learning tasks. Although the selection of relevant features has been extensively studied in supervised learning, feature selection with the absence of class labels is still a challenging task.

            This paper develops a novel method for unsupervised feature selection, which efficiently selects features in a greedy

            Backward Greedy Feature Extraction

            Forward Greedy Feature Extraction

            Classification Algorithm (KNN, SVM, RF, NBC, J48)

            Metrics and Accuracy Analysis

            manner. The paper first defines an effective criterion for unsupervised feature selection which measures the reconstruction error of the data matrix based on the selected subset of features. The paper then presents a novel algorithm for greedily minimizing the reconstruction error based on the features selected so far. The greedy algorithm is based on an efficient recursive formula for calculating the reconstruction error.

            The greedy algorithm selects iteration the most representative feature among the remaining features, and then eliminates the effect of the selected features from the data matrix. This step makes it less likely for the algorithm to select features that are similar to previously selected features, which accordingly reduces the redundancy between the selected features. In addition, the use of the recursive criterion makes the algorithm computationally feasible and memory efficient compared to the state of the art methods for unsupervised (forward and backward) feature selection.

            Predict Heart Disease Patient

            Fig 4.1 Architecture Diagram Of Proposed System

          4. PSO FEATURE EXTRACTION MODEL

            Feature selection is the process of identifying statistically most relevant features to improve the predictive capabilities of the classifiers. To find the best features subsets, the population based approaches like Particle Swarm Optimization (PSO) and genetic algorithms are being widely employed. However, it is a general observation that not having right set of particles in the swarm may result in sub-optimal solutions, affecting the accuracies of classifiers.

            The feature selection algorithms have been widely used in many application areas such as genomic analysis, text classification], information retrieval, and bio informatics. Feature selection is an optimization problem which aims to determine an optimal subset of d features out of n features in the input data (d << n). That maximizes the classification or prediction accuracy. Performing an exhaustive candidate feature subsets, based on some evaluation criterion, is computationally infeasible, and it becomes an NP-hard problem with the increasing n value.

          5. CLASSIFICATION ANALYTICAL MODEL

          Machine learning indicates how computers can learn or improve their performance using data. Computer programs to automatically learn to identify patterns and make intelligent based decisions on data. Machine learning is a fast growing discipline. Here, using classic problems in machine learning that are highly related to data mining.

          • Supervised classification learning:

            • Supervised classification learning model consist of all data is labeled and algorithm learn to predict the output from training dataset.

            • E.g.: Support Vector Machine (SVM), Random

              Forest Naive Bayes.

          • Unsupervised classification learning

            • Unsupervised classification learning is used for clustering based algorithm. In this learning all the data is unlabelled and algorithm learns to essential structure from the input dataset. We can use clustering to discover classes within

              the dataset.

            • E.g. K-means, KNN. Neural Networks

          • Semi-supervised classification Learning

            • Semi-supervised learning is a combination of supervised learning and unsupervised learning. In Semi-

          supervised learning some data is labeled and some data is not labeled. In this approach, labeled training dataset are used to learn class models and unlabelled training dataset are used to define the boundaries between classes.

        5. RESULTS AND DISCUSSIONS

          1. DATASET DESCRIPTION

            Attributes Type

            Description

            Gender Categorical

            age

            age given in years

            Real number

            Sex

            sex (Value 1 : male; Value 0 : female)

            String

            Cp

            chest pain type(1: typical angina ; 2: atypical angina

            3: non-anginal pain ;4: asymptomatic)

            Real number

            Trestbps

            resting blood pressure (in mm Hg on admission to the hospital)

            Real number

            Chol

            Cholestoral(Serum cholestoral) in mg/dl

            Real number

            Fbs

            Fasting blood sugar in mg/dl (>120)

            Value 1 = true; Value 0

            = false)

            Real number

            Attributes Type

            Description

            Gender Categorical

            age

            age given in years

            Real number

            Sex

            sex (Value 1 : male; Value 0 : female)

            String

            Cp

            chest pain type(1: typical angina ; 2: atypical angina

            3: non-anginal pain ;4: asymptomatic)

            Real number

            Trestbps

            resting blood pressure (in mm Hg on admission to the hospital)

            Real number

            Chol

            Cholestoral(Serum cholestoral) in mg/dl

            Real number

            Fbs

            Fasting blood sugar in mg/dl (>120)

            Value 1 = true; Value 0

            = false)

            Real number

            Preparing the database – for obtaining the result, this paper used Heart patient data sets from ILPD (Indian Heart Patient) Data Set (table 5.1). Totally, heart dataset has 583 samples which holds 10 independent variables and one dependent variable. Independent Variables are: Age, Gender, Total Bilirubin, Direct Bilirubin, Total Proteins, Albumin, SGPT (serum glutamic-pyruvic transaminase), SGOT (serum glutamic oxaloacetic transaminase), Alkaline Phosphatase and one dependent variable is Classing (class) [36].

            Restecg

            Resting electrocardiographic results

            Real number

            Thalach

            Heart rate achieved at maximum

            Integer

            Exang

            Exercise induced angina (Value 1 : yes; Value 0 : no)

            Integer

            Oldpeak

            ST depression originated by exercise relative to rest

            Integer

            Slope

            Slope of the peak exercise ST segment (Value 1:

            upsloping ; Value 2: flat ; Value 3: downsloping )

            Integer

            Ca

            Major vessels (0-3) colored by flouroscopy

            Integer

            Thal

            Result of thalium stress test (Value 3 = normal; Value 6 = fixed defect; Value 7 = reversable defect )

            Integer

            Num

            status of heart disease (angiographic status) Value 0: < 50% diameter narrowing Value 1: > 50% diameter narrowing

            Binary

            Table 5.1 Dataset Attribute

            The following table 5.2 describes the feature extraction attributes

            Attributes Type

            Description

            Gender Categorical

            Trestbps

            resting blood pressure (in mm Hg on admission to the hospital)

            Real number

            Chol

            Cholestoral(Serum cholestoral) in mg/dl

            Real number

            Fbs

            Fasting blood sugar in mg/dl (>120)

            Value 1 = true; Value 0 = false)

            Real number

            Restecg

            Resting electrocardiographic results

            Real number

            Thalach

            Heart rate achieved at maximum

            Integer

            Exang

            Exercise induced angina (Value 1 : yes; Value 0 : no)

            Integer

            Oldpeak

            ST depression originated by exercise relative to rest

            Integer

            Slope

            Slope of the peak exercise ST segment (Value 1: upsloping ; Value 2: flat ; Value 3: downsloping )

            Integer

            Table 5.2 Feature Selection Dataset

          2. PERFORMANCES METRICS ANALYSIS

            Table 5.2 describes an evaluation metrics for Heart disease prediction model. The table contains Mean Absolute error, Root Relative square Error, Root Relative Square Error and Accuracy values of SVM, KNN, RF, J.48 and MLP classification algorithm details are shown

            Table 5.2 Performances Analysis

            Evaluation Criteria

            SV M

            KN N

            RF

            J.48

            ML P

            Mean Absolute Error (MAE)

            0.34

            5

            0.32

            3

            0.33

            7

            0.31

            7

            0.32

            2

            Root Mean Square Error (RMSE)

            0.45

            2

            0.38

            6

            0.43

            2

            0.38

            2

            0.39

            3

            Relative Absolute Error (RAE)

            0.52

            3

            0.48

            2

            0.50

            2

            0.48

            5

            0.49

            2

            Root Relative Square Error (RRSE)

            0.47

            8

            0.42

            8

            0.43

            8

            0.40

            2

            0.41

            8

            Accuracy

            0.88

            2

            0.89

            2

            0.87

            4

            0.93

            4

            0.92

            6

            Fig 5.2 describes an evaluation metrics for Heart disease prediction model. The table contains Mean Absolute error and Root Relative square Error of SVM, KNN, RF, J.48 and MLP classification algorithm details are shown

            MAE and RMSE Analysis

            MAE and RMSE Analysis

            0.6

            0.5

            0.4

            0.3

            MAE

            RMSE

            0.6

            0.5

            0.4

            0.3

            MAE

            RMSE

            0.2

            0.1

            0

            Classification Algorithm

            SVM KNN RF J.48 MLP

            0.2

            0.1

            0

            Classification Algorithm

            SVM KNN RF J.48 MLP

            Error

            Error

            Fig 5.1 MAE and RMSE Analysis

            Fig 5.2 describes an evaluation metrics for Heart disease prediction model. The table contains Root Relative square Error and Root Relative Square Error of SVM, KNN, RF, J.48 and MLP classification algorithm details are shown

            RAE and RRSE Analysis

            0.6

            0.5

            0.4

            0.3

            0.2

            RAE

            RRSE

            RAE and RRSE Analysis

            0.6

            0.5

            0.4

            0.3

            0.2

            RAE

            RRSE

            0.1

            0

            0.1

            0

            SVM KNN RF J.48 MLP

            Classification Algorithm

            SVM KNN RF J.48 MLP

            Classification Algorithm

            Error

            Error

            Fig 5.2 RAE and RRSE Analysis

            Accuracy(%)

            Accuracy(%)

            Fig 5.3 describes an evaluation metrics for Heart disease prediction model. The table contains accuracy values of SVM, KNN, RF, J.48 and MLP classification algorithm details are shown

            Accuracy Analysis

            Accuracy Analysis

            0.94

            0.93

            0.92

            0.91

            0.9

            0.89

            0.88

            0.87

            0.86

            0.85

            0.84

            Accuracy

            0.94

            0.93

            0.92

            0.91

            0.9

            0.89

            0.88

            0.87

            0.86

            0.85

            0.84

            Accuracy

            SVM KNN RF J.48 MLP

            Algorithm

            SVM KNN RF J.48 MLP

            Algorithm

            Fig 5.3 Accuracy Analysis

        6. CONCLUSION

          The proposed technique is producing an enhanced concept over the heart disease prediction within novel data mining techniques; SVM, RF, NB, MLP and j48 the weighted association classifier. The PSO is an enhance approach for classification with weighted association rule with Weighted Support and Confidence Framework to extract Association rule from big data warehouse. J48 classification is the technique to cluster the attributes from the patient record. The SVM clustering and J48 with weighted association classifier can enhance the classification performance and accuracy of the heart disease diagnosis.

          These all metrics is being verified by the experts and professional doctors of heart specialist. The proposed technique PSO is producing 93.46% accuracy for centroid selection and classification, PSO is producing 93.40% accuracy for classification

          with weighted support and confidence. The overall accuracy of the system is 94.40%

          In future work, we will look for the more enhancements to get better result over the heart disease prediction by increasing the metrics and doctors suggestion within different types of medical term and also provide the first-aid suggestion in unavailability of heart specialist or experts.

        7. REFERENCES

    1. J.Vijayashree and N.Ch.Sriman Narayana Iyengar, Heart Disease Prediction System Using Data Mining and Hybrid Intelligent Techniques: A Review , International Journal of BioScience and Bio Technology, Vol.8, No.4 (2016), pp. 139-148.

    2. Dilip Roy Chowdhury, Mridula Chatterj ee & R. K. Samanta, An Artificial Neural Network Model for Neonatal Disease Diagnosis, International Journal of Artificial Intelligence and Expert Systems (IJAE), Volume (2): Issue (3), 2011.

    3. Milan Kumari, Sunila Godara, Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction, IJCST Vol. 2, Iss ue2, June 2011.

    4. Ishtake S.H ,Prof. Sanap S.A., Intelligent Heart Disease Prediction System Using ata Mining Techniques, International J. of Healthcare & Biomedical Research,2013.

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    7. Kiran, M.; Murphy, P.; Monga, I.; Dugan, J.; Baveja, S.S. Lambda architecture for cost-effective batch and speed big data processing. In Proceedings of the 2015 IEEE International Conference on Big Data,Santa Clara, CA, USA, 29 October1 November 2015; pp. 27852792.

    8. Cheryl Ann Alexander and Lidong Wang, Big Data Analytics in Heart Attack Prediction, JNurs Care, an open access journal, Volume 6 , Issue 2, ISSN:2167-1168,2017.

    9. Ms. S.Suguna, Sakthi Sakunthala,N, S.S anjana,S.S.Sanjhan, A Survey On Prediction of Heart Diseases Using Big Data Algorithms, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 6, Issue 3, March 2017,ISSN:22781323.

    10. Saranya P and Satheeskumar B A Survey on Feature Selection of Heart Disease Using Data Mining Techniques, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, May- 2016, pg. 713-719.

    11. Vinitha S, Sweetlin S, Vinusha H and Sajini S. Disease Prediction Using Machine Learning Over Big Data, Computer Science & Engineering: An International Journal (CSEIJ), Vol.8, No.1, February 2018.

    12. Kelvin KF Tsoi1, Yong-Hong Kuo and Helen M. Men, Dmitry Ignatov and Andrey Ignatov. Decision Stream: Cultivating Deep Decision Trees, 3 Sep 2017 IEEE.

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