ARCHIVES

Heart disease is one of the leading causes of death worldwide, making early prediction very important. This project develops a machine learning-based system to predict heart disease using patient data such as age, blood pressure, and cholesterol levels. Various algorithms including Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest are applied and compared. The dataset is preprocessed using scaling techniques and divided into training and testing sets. Model performance is evaluated using accuracy, confusion matrix, and ROC curve. A majority voting method is also used to improve prediction reliability. The system allows real-time prediction using user input. The results show that machine learning can help in early detection and support doctors in making better decisions.