Progressive Heart Disease Prediction Model Using Machine Learning: A Comprehensive Staging Approach

This research delves into employing machine learning algorithms—Decision Tree, KNN, and Random Forest Classifier—to accurately predict heart disease likelihood and stages based on patient attributes. Using a meticulously curated dataset comprising 11 medical attributes, the study highlights Random Forest as the most efficient algorithm, boasting an impressive 87.12% accuracy rate. The overarching goal of this investigation is to combat the global health challenge of heart disease, emphasizing the critical need for early and precise predictions to significantly enhance patient outcomes. By amalgamating recent advancements in disease prediction models, the study presents a robust methodology encompassing comprehensive data collection, model development, and meticulous evaluation. Rigorous data preprocessing techniques lead to the development of Decision Tree, KNN, and Random Forest models, where detailed model evaluation metrics and disease staging identify Random Forest as the most accurate predictor. The workflow design within this research demonstrates a systematic approach—from user input collection to nuanced disease prediction and staging, emphasizing the consideration of vital health parameters for a comprehensive understanding of cardiovascular health. The results showcase the model's prowess in predicting heart disease presence, classifying individuals into distinct stages, and specifying disease types, thus offering crucial insights into the nature and severity of detected heart conditions. The study's future enhancements focus on practical applications, envisioning the development of user-friendly interfaces, real-time monitoring systems, predictive analytics integration, and device synchronization. These improvements aim to enhance accessibility, usability, and personalized health insights, paving the way for proactive healthcare interventions and holistic management of cardiovascular health.