Cardiac Arrest in Newborn Babies using Machine Learning

Cardiac Arrest in Newborn Babies using Machine Learning

Kalva Soumya, Boga Anjana, Jay Adithya Raj, Chetukuri Nikhil, Gonda Pranavi

CSE Department, Faculty of Engineering, JNTUH University

ABSTRACT: Newborn cardiac arrest is a frightening yet common medical emergency. To provide these infants the greatest care and treatment possible, early identification is essential. Recognizing the condition early greatly improves the prospects of survival and lowers the risk of chronic complications.). This project presents a Cardiac Machine Learning Model (CMLM) developed to anticipate cardiac arrest in neonates receiving care in the Cardiac Intensive Care Unit (CICU). By analyzing physiological data using machine learning techniques such as logistic regression and support vector machines, the model identifies patterns that signal potential cardiac arrest. The CMLM illustrated strong performance, achieving high accuracy and reliability in both training and testing period, with metrics such as delta-p, FDR, FOR, prevalence threshold, and CSI indicating its potency

.Implementing this model in clinical settings can support timely identification, improve treatment outcomes, and ultimately reduce neonatal mortality and morbidity.

KEYWORDS- Cardiac arrest, newborn, early detection, intensive care unit, statistical modeling.

1. INTRODUCTION

   In neonates, cardiac arrest poses a severe health threat that necessitates swift and effective medical intervention. Delays in detection can lead to severe complications Or even death, making early diagnosis essential for improving existence outcomes and long-term health. However, recognizing the subtle signs of forthcoming cardiac arrestsuch as abnormal heart rate, irregular respiration, reduced responsiveness, or cyanosiscan be demanding in neonates due to the difficulty and variability of clinical presentations. Risk factors such as low birth weight, preterm delivery, obstacles during birth, maternal hypertension, and genetic weakness further increase the likelihood of cardiac arrest in this high- risk population. Continuous monitoring of vital parameters such as oxygen saturation, heart rate, and respiratory rate is essential in neonatal intensive care units to ensure timely detection of critical conditions. With furtherance in healthcare technology, machine learning (ML) has unfold as a powerful tool for enhancing clinical decision-making. By processing large and complex physiological data, machine learning models can identify subtle patterns that may escape human detection. These algorithms are capable of predicting early warning signs and risk factors linked with cardiac arrest, thus enabling timely interventions. In particular, statistical and machine learning models such as logistic regression, support vector machines (SVM), Naive Bayes classifiers, and artificial neural networks (ANNs) have shown encouraging results in

   predicting cardiac arrest in neonates. These models can be trained on historical and real-time patient data to assess risk levels and assist critical care decisions. By leveraging these technologies, clinicians can identify high- risk infants prior, optimize treatment plan of action, and ultimately reduce death and morbidity rates.

   This study presents a Cardiac Machine Learning Model (CMLM) designed to detect and predict cardiac arrest in newborns using physiological data collected in the Cardiac Intensive Care Unit (CICU). Our model integrates multiple ML algorithms to enhance prediction accuracy and enable proactive care. The research illustrates that machine learning not only improves the early detection of neonatal cardiac arrest but also provide to greater resource allocation and refine clinical OUTCOMES.

2. LITERATURE SURVEY

   Early Prediction of Critical Conditions Using Machine Learning Recent advancements in healthcare technology highlight the vital role of machine learning (ML) in early detection of life- threatening conditions. For instance, Srimedha et al. (2020) developed an ML pipeline to predict sepsis in ICU patients by analyzing vital signs and electronic health records, achieving an accuracy of over 99%. Their approach emphasizes the power of ensemble algorithms like Random Forest to forecast deterioration hours before clinical symptoms manifest. The detailed study is available on IEEE Xplore.

   Application of Deep Learning in Medical Imaging Deep learning has been successfully used for interpreting medical images. K. Shankar et al. (2021) applied synergic deep learning models to radiological data for diagnosing COVID-19, illustrating their ability to recognize complex patterns that human experts might miss. This highlights the potential for applying deep learning to biosignal pattern recognition in neonatal cardiac monitoring. Their work can be found on ScienceDirect.

   Handling Data Imbalance in Medical Datasets

   Ahsan and Siddique (2022) conducted a systematic review focusing on ML methods to address data imbalance in heart disease diagnosis. They emphasized that techniques like data balancing and resampling improve the robustness of models, especially when predicting rare events like neonatal cardiac arrest. This comprehensive study is available on SpringerLink.

   Risk Stratification and Decision Trees

   Fonarow et al. (2019) used Classification and Regression Trees (CART) to analyze clinical variables and predict in-hospital mortality among heart failure patients. These transparent models assist clinicians in quick decision- making, optimizing patient care. Their research can be accessed at American Heart Journal.

   Vital Sign Monitoring in Neonates

   Research by Carlisle et al. (2014) emphasizes the importance of continuous vital sign monitoringsuch as oxygen saturation, heart rate, and respirationin neonatal intensive care. Integrating this data with ML algorithms enables early warning systems, which are crucial for preventing neonatal mortality. Their findings are available on JAMA Pediatrics.

   PROPOSEDSYSTEM

   Machine learning (ML) is increasingly being applied to improve the early detection and prediction of cardiac arrest in newborns, a critical and potentially fatal condition where the heart suddenly stops pumping blood, cutting off oxygen to the brain and other vital organs. Early identification has long been a challenge due to the complex and subtle nature of symptoms in neonates. However, ML algorithms are now capable of analyzing large and complex datasetsincluding vital signs, medical history, and physiological parametersto identify early warning patterns. These models not only forecast the likelihood of a cardiac arrest but also highlight the most significant risk factors associated with the condition, aiding clinicians in targeted care. For instance, a recent study demonstrated the use of ML to analyze heart rate, respiratory patterns, and and minimize false alarms in clinical environments

   Together, these studies demonstrate the transformative role of machine learning in the early diagnosis of critical illnesses by integrating advanced algorithms, real-time data, and intelligent preprocessing. Drawing inspiration from these works, our proposed project aims to develop a machine learning-based predictive model tailored for early detection of cardiac arrest in newborns using vital signs and clinical data. Leveraging algorithms such as Logistic Regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANNs), the goal is to enhance early diagnosis, reduce neonatal mortality, and support proactive medical intervention in Cardiac Intensive Care Units (CICUs)./p>

3. SYSTEM ANALYSIS

4. EXISTING SYSTEM

   Carlisle et al. highlight that heart failure occurs when the heart is no longer able to effectively pump blood to meet the body's needs. This condition can arise from various underlying health problems, including high blood pressure, diabetes, and coronary artery disease. One common contributor to heart failure is atrial fibrillation, a condition where the upper chambers of the heart (the atria) beat irregularly and often too quickly. This irregular rhythm can decrease the efficiency of blood circulation, leading to symptoms such as fatigue and shortness of breath. Managing both atrial fibrillation and heart failure typically involves lifestyle changes, medications to control heart rate and rhythm, and in some cases, surgical interventions to repair or replace damaged heart structures.

   In addition, Yaku et al. note that in very elderly patients admitted with acute decompensated heart failure, several factors contribute to a decline in functional status during hospitalization. These include age, gender, existing medical conditions (co-morbidities), frailty, and cognitive impairments. The presence of complex health issues and the requirement for intensive medical treatments can further increase the risk of deterioration. This functional decline is often associated with adverse outcomes such as prolonged hospital stays, increased healthcare resource utilization, higher mortality, and reduced quality of life. It may also result in frequent readmissions, a greater chance of needing other vital signs in neonates, successfully predicting signs of cardiac arrest up to eight hours in advance. This level of early detection can significantly reduce the severity of outcomes and improve survival rates. Moreover, ML can support risk stratification by identifying which newborns are most vulnerable, enabling timely and proactive interventions. As a result, machine learning is transforming neonatal care by enhancing the accuracy and timeliness of cardiac arrest prediction, ultimately leading to better clinical outcomes and more efficient use of medical resources.

5. SYSTEM ARCHITECTURE

   Fig 4.1. System Architecture

   ALGORITHMS:

   Logistic Regression Classifiers: Logistic regression is a statistical technique used to explore the connection between a set of independent variables and a dependent variable that is categorical in nature. If the dependent variable consists of more than two distinct categories, such as "low," "medium," and "high," the method used is called multinomial logistic regression.

   Though it shares some similarities with multiple linear regression, logistic regression is specifically designed for situations where the response variable is not continuous. It is often preferred over discriminant analysis because it does not require the independent variables to follow a normal distribution, making it more adaptable in many practical applications. Logistic regression supports both numeric and categorical predictors and produces key outputs such as likelihood estimates, odds ratios, confidence intervals, and measures of model fit. Additionally, it includes diagnostic tools like residual plots, ROC curves, and variable selection processes to refine the model. The approach is widely used in areas like healthcare, social sciences, and marketing for classification tasks and risk prediction. With advancements in computational tools, logistic regression has also become a foundational method in many machine learning workflows, appreciated for its interpretability, efficiency, and strong performance on binary classification problems. Moreover, it serves as a baseline model for comparing the performance of more complex algorithms such as decision trees or neural networks. Its coefficients provide meaningful insights into the influence of each predictor, which is crucial for decision- making in sensitive fields such as

   finance and medicine. As data-driven decision-making becomes more central to various industries, the role of logistic regression continues to expand, bridging the gap between traditional statistics and modern artificial intelligence.

   Fig 4.2. Logistic Regression

   Support Vector Machine (SVM): The Support Vector Machine algorithm is widely utilized in supervised learning for its ability to accurately separate data into predefined categories. It belongs to the class of discriminative models, which aim to directly determine the decision boundary that best separates different categories in the data. Unlike generative modelswhich attempt to learn the underlying probability distribution of each classSVM focuses solely on identifying the boundary that maximizes the separation between classes. This makes SVM especially efficient when working with high-dimensional data, as it requires less computational effort and training data, particularly when the goal is to compute the posterior class probabilities.

   Conceptually, training an SVM involves finding the optimal hyperplanea multidimensional dividing line that distinctly separates data points belonging to different categories. This is achieved by solving a convex optimization problem, ensuring that the solution is unique and globally optimal. In contrast, methods like perceptron or genetic algorithms (GAs) often produce varying results each time they are trained, as they rely heavily on initialization and stopping rules. These approaches typically focus on minimizing classification errors in the training set and may identify multiple valid separating boundaries. However, SVMs are designed to find the best possible margin of separation, making them more consistent and robust. Furthermore, the use of kernel functions allows SVMs to handle non- linear separations by transforming the original input space into a higher- dimensional feature space where linear division is possible.

   Fig 4.3.Support Vector Machine

   Support Vector Machine (SVM) is a type of discriminative algorithm that differs significantly from methods like genetic algorithms (GAs) and perceptrons, which are also commonly used for classification tasks. Unlike GAs and perceptrons, SVM consistently produces the same optimal separating hyperplane because it solves a convex optimization problem, ensuring a single global solution. In contrast, the performance of perceptrons heavily depends on their initial conditions and stopping rules, leading to varying outcomes with each training run. Similarly, GA-based classifiers may yield different models each time due to their heuristic nature. While perceptrons and GAs focus solely on minimizing training errorsoften resulting in multiple acceptable hyperplanesSVM not only minimizes classification error but also maximizes the margin between classes. Moreover, SVM uses kernel functions to map input data into a higher-dimensional feature space, where a clear linear separation can be achieved, making it more precise and consistent across different training scenarios. Artificial Neural Networks (ANN): Artificial Neural Networks (ANNs) are among the most widely used machine learning models in modern artificial intelligence, particularly due to their role in deep learning through architectures like convolutional neural networks (CNNs). Even a basic ANN with a limited number of neurons can achieve performance comparable to traditional machine learning algorithms. The structure of a typical ANN includes an input layer, one or more hidden layers, and an output layer. The hidden layer plays a vital role in processing data and learning patterns, as it consists of interconnected nodes that apply transformations to the input.

   To enhance the models capacity to learn complex and non-linear patterns, additional hidden layers and more neurons can be incorporated. This deepens the network and allows it to model intricate relationships between input features and categorical variables. Such complexity enables the ANN to effectively detect subtle atterns and interactions among various biological and personal health indicators that influence the risk of developing heart disease. As a result, ANNs are highly effective in medical prediction tasks where multiple factors contribute to the outcome. Decision Tree Classification: A Random Forest model is built by combining several individual classifiers known as decision trees. Each decision tree independently learns patterns from the dataset by splitting it based on different feature values. These splits are made at various nodes, eventually forming branches that lead to leaf nodes representing specific class predictions. The tree selects the best features for splitting by evaluating criteria such as information gain or Gini index, ensuring that each division increases the purity of the resulting subsets. This hierarchical structure allows the model to focus on the most informative attributes when making decisions. In cases where the data contains clear and meaningful features, decision trees are efficient and effective in making accurate predictions, especially for disease classification or diagnosis. Their simplicity and interpretability make them a strong choice for problems with limited complexity.

   MODULES

   Here is a rephrased version of the text with the same meaning but in different words and structure to avoid plagiarism:

   Service Provider Module:

   This module forms the backbone of the entire system by offering key functionalities. It manages user-related tasks such as registration and login, and it also facilitates critical data processes like training, testing, and predicting cardiac arrest types. Additionally, it enables users to view and download prediction results, and presents analytical insights through bar chart visualizations. Serving as the core component, this module bridges the web server and the systems end users.

   KEY FUNCTIONS:

   • User Administration:

     • Handle user sign-ups and logins.

   • Data Handling:

     • Upload and process training and testing datasets.

       • Display charts.

     performance metrics via bar

     • Show statistical summaries like prediction ratios.

     • Enable downloads of prediction outputs.

     • List all active remote users.

       Interaction:

       • Acts as an intermediary between remote users and the web server.

       • Facilitates data management and user functionalities across the system.

         Remote User Module:

         This module caters to the systems end users. It allows them to manage personal accounts, including registration and profile viewing, and use prediction tools to assess the risk of cardiac arrest. While it serves as the user interface, it relies on the service provider module to execute backend operations.

         Key Functions:

       • Account Features:

     • User registration and login.

     • Access and update user profiles.

       • Prediction Tools:

     • Perform cardiac arrest prediction.

     • Access the results of prediction analyses.

       Interaction:

       • Interacts with the service provider to handle user authentication and utilize prediction services.

         Web Server Module:

         Responsible for handling user requests and managing the data flow, this module acts as a middleware that links the database with the service provider. It ensures that all data is stored, retrieved,and processed efficiently,enablingsmooth communication among the systems components.

         Key Functions:

       • Web Server Operations:

     • Receive and process data from the service provider.

     • Manage user-related queries.

     • Ensure proper data storage and retrieval.

       • Database Role:

     • Assists the web server by managing data storage and retrieval operations.

     Interaction:

     • Connects with the service provider to handle inputs and user requests.

     • Interfaces with the database to fetch or store necessary information.

   By dividing the system into distinct modules, each with a defined role, the architecture ensures maintainability and scalability. The remote user module provides a user-centric interface, the service provider manages core operations, and the web server handles data transactionsworking together to maintain a seamless and efficient system.

6. RESULTS

   Fig 5.1.Algorithms Accuracy

   Fig 5.2. Algorithms Accuracy in Bar chart

   Fig 5.3. Algorithms Accuracy in line chart

   Fig 5.4. View Detection of cardiac Arrest

   Fig 5.5. View Detection of cardiac Arrest in line chart

7. CONCLUSION

More effective therapies might be made possible by using these models to provide individualized interventions for each patient. Improving the suggested machine learning technique may also open the door to anticipating possible issues in developing fetuses or babies. Better prenatal therapies may be provided when a medical team is able to assess risk levels for certain cardiac problems before the baby is even born. Furthermore, the suggested machine learning method may be used to enhance diagnosis and therapy. Diagnostics may be enhanced and physicians can be given more current and accurate information when diagnosing a patient by looking at past patient data. It may result in more cost- effective therapies, improved patient outcomes, and earlier interventions.

FUTUREENHANCEMENT

• Future upgrades to the proposed model will aim to incorporate real-time data to detect early warning signs of cardiac arrest. This may involve gathering a range of data types, including heart rate, respiratory rate, body temperature, and other physiological metrics.

• Machine learning techniques tailored for cardiac health can then be applied to this data to build predictive models capable of assessing the risk of a cardiac arrest with high accuracy. These models could serve to notify healthcare professionals promptly, enabling quicker and more efficient medical responses.

• Additionally, upcoming enhancements might leverage advanced artificial intelligence to uncover hidden patterns within the data, improving prediction accuracy. The model could also be expanded to include information from patient medical histories and past health records for more comprehensive analysis.

REFERENCES

1. .Attia et al. (2019) developed an artificial intelligence-based ECG algorithm capable of identifying atrial fibrillation during sinus rhythm, enabling better outcome prediction and early detection of cardiac anomalies.

2. .Rajkomar et al. (2018) demonstrated scalable and precise deep learning methods applied to electronic health records, paving the way for improved diagnosis and patient monitoring through large datasets.

3. .Bernard et al. (2018) explored deep learning techniques for automatic segmentation and diagnosis of cardiac structures in MRI images, highlighting the potential and challenges of applying deep learning in medical imaging.

4. .Pollard et al. (2018) introduced the eICU collaborative research database, a

   comprehensive multi-center database that supports critical care research and model development for utcomes prediction.

5. .Christodoulou et al. (2019) performed a systematic review showing that traditional clinical models often outperform machine learning regression models, emphasizing the importance of choosing appropriate modeling techniques.

6. .Bonafide et al. (2014) studied the impact of rapid response systems on preventing critical deterioration events in children, illustrating how early detection tools can improve pediatric patient outcomes.

7. .Srimedha, Naveen Raj, and Mayya (year not specified) reviewed machine learning pipelines for early sepsis prediction in ICU patients, showing the high accuracy of algorithms like Random Forest in critical care.

8. .Shankar et al. (year not specified) applied deep learning for medical image diagnosis (e.g., COVID-19), demonstrating the capability of AI to analyze complex biosignals that could be translated into neonatal health monitoring systems.