Heart Disease Prediction Using Machine Learning This repository contains a machine learning project focused on predicting the presence of heart disease based on various patient attributes and clinical measurements. The project utilizes a dataset with 13 features, including demographic information and medical test results.
Project Overview The goal of this project was to explore and compare the performance of different machine learning models for predicting heart disease. The models used in this project include Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting, XGBoost, AdaBoost, Naive Bayes, and MLP Neural Network.
Key Features Data Exploration: Analyzed distributions and correlations among features using visualizations. Data Preprocessing: Handled missing values and standardized numerical features. Model Training and Evaluation: Trained each model on the dataset and evaluated their performance using accuracy, precision, recall, and F1-score metrics. Model Comparison: Compared the performance of all models to identify the best-performing one for heart disease prediction. Results Logistic Regression: Achieved an accuracy of 0.80. Decision Tree: Achieved an accuracy of 0.99 with high precision and recall. Random Forest: Achieved an accuracy of 0.99, demonstrating robust performance. SVM: Achieved an accuracy of 0.89. KNN: Achieved an accuracy of 0.83. Gradient Boosting: Achieved an accuracy of 0.93. XGBoost: Achieved an accuracy of 0.99. AdaBoost: Achieved an accuracy of 0.88. Naive Bayes: Achieved an accuracy of 0.80. MLP Neural Network: Achieved an accuracy of 0.93.