🫀 Cardiovascular Disease Prediction using Machine Learning This project aims to predict the presence of cardiovascular (heart) disease using a dataset of 1,025 patient records containing genetic, lifestyle, and clinical features. Several supervised machine learning algorithms were implemented, including:
Logistic Regression
Decision Tree
Random Forest
Support Vector Machine (SVM)
K-Nearest Neighbors (KNN)
Naïve Bayes
XGBoost
🔍 Key Features: Comprehensive data preprocessing (normalization, encoding, outlier handling)
Comparative evaluation using accuracy, precision, recall, and F1-score
Confusion matrix analysis to assess misclassification
Random Forest and XGBoost achieved the highest accuracy (~98%)
📈 Goal: To build an accurate, interpretable, and efficient heart disease prediction model that supports early diagnosis and clinical decision-making.