Early Detection and Accurate Prediction of Heart Disease and Recommendations for Effective Prevention using Ensemble method

Heart disease is one of the leading causes of death globally, responsible for millions of deaths each year. Heart disease remains a leading cause of morbidity and mortality worldwide. Early detection and accurate prediction of heart disease risk factors are crucial for effective prevention and timely intervention. In recent years, machine learning techniques have emerged as powerful tools for predictive analytics in healthcare. This paper focuses on predicting the presence of heart disease in patients by utilizing Classification machine learning algorithms: Support Vector Machine (SVM), Naive Bayes, XG Boost and Random Forest. Each model analyzes a set of input features, including age, sex, chest pain type (cp), resting blood pressure (trestbps), cholesterol levels (chol), fasting blood sugar (fbs), resting electrocardiographic results (restecg), maximum heart rate achieved (thalach), exercise-induced angina (exang), oldpeak, slope of the peak exercise ST segment (slope), number of major vessels (ca), and thalassemia status (thal). The performance of these models is compared to determine the most accurate method for classification into two categories: patients with heart disease (1) and those without (0). Ultimately, the Random Forest algorithm is selected as the best-performing model, leveraging its simplicity and interpretability for accurate prediction. Additionally, the system offers tailored recommendations for patients, including medication guidance, precautionary measures, heart-healthy dietary plans, suitable workouts, and specific food choices to promote cardiovascular health. This predictive tool aims to enhance the decision-making process for healthcare providers by identifying at-risk individuals, providing insights for lifestyle changes and medical interventions, and facilitating timely preventive care.