Heart attack prediction model using machine learning algorithms Department:Computer Science By: SIRJOSEPH Project ID: 8964 Rating: (5.0) votes: 1 Price:₦4000 Get the Complete MaterialAbstractCardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, with heart attacks (myocardial infarctions) accounting for a significant proportion of these deaths. Traditional diagnostic methods, such as electrocardiograms and blood test,s are predominantly reactive, often detecting issues only after symptoms appear, resulting in delayed interventions and increased mortality risk. This research proposes a proactive approach by developing a machine learning (ML)-based model for the early prediction of heart attacks using diverse, multi-source healthcare data. The study aims to design and implement a robust, accurate, and interpretable ML model that leverages electronic health records (EHRs), wearable device data, and clinical test results to deliver personalized risk assessments. A comprehensive literature review reveals that ML algorithms such as Random Forest, XGBoost, and deep neural networks significantly outperform traditional statistical models in prediction accuracy, achieving up to 94% sensitivity in clinical trials. However, current challenges in data quality, model interpretability, and clinical integration limit their practical applicability. To address these challenges, this study employs advanced data preprocessing techniques, including feature engineering, synthetic oversampling, and multimodal data fusion, alongside model selection strategies that prioritize both accuracy and explainability. The performance of the proposed model is validated using cross-validation and holdout techniques on publicly available datasets, such as the UCI Heart Disease dataset. In addition, the research explores the integration of explainable AI methods to enhance transparency and foster clinical trust. Ultimately, this project contributes to the field by bridging the gap between high-performing ML models and real-world clinical applicability. By focusing on interpretability, data diversity, and healthcare integration, the proposed model aims to facilitate earlier diagnosis, support personalized treatment strategies, and reduce the global burden of heart disease....Preview Download Preview +Other Computer Science project topics and materials you might be interested in»Design and Implementation of digital library system»Design and Implementation of a Virtual E-Learning System ( Case study of Lagos State University)»Design and Implementation of Student Project Management and Allocation System»Design and Implementation of online cash receipt generating system for a supermarket»Design and Implementation of a Software Result Processing and Transcript Generation System»Design and Implementation of computerized hospital database management system»Design and Implementation of a Hotel Database Management System and Service ( A Case Study of Winter Suites and Hotels, Owerri)»Design and Implementation of Online Birth and Death Registration System»Design and Implementation of Online Clearance System»Design and Implementation of Loan Management System with SMS Notification»Design and Implementation of Computerized Staff Record Department»Design and Implementation of Cyber Cafe Security»Design and Implementation of N.Y.S.C Posting System ( A case study of N.Y.S.C Enugu)»Design and Implementation of a Computerized Fraud Detection in a Bank»Design and Simulation of a Secured Wireless Network