Detection of Myocardial Infarction Using Ensemble Classifiers

The system collects various inputs including type of chest pain, gender, resting blood pressure, age, blood sugar level after fasting, resting electrocardiogram (ECG), cholesterol and maximum heart rate. After the user provides all these inputs, the system will detect if there is any heart disease. It processes signals through image processing techniques to isolate essential waves, converting them into a CSV file for analysis. Various supervised classification algorithms are employed, including Logistic Regression, Support Vector Machine (SVM), k-nearest neighbors (KNN), and the Voting-Based Ensemble Classifier, for analysis based on CSV data. Implement a virtual assistant capable of answering heart-related health questions and providing immediate support and guidance. It will also provide nearby hospitals that specializes in heart disease. Offer personalized lifestyle recommendations such as diet plans, exercise routines, and techniques for managing stress to mitigate the risk of heart disease.