Heart Disease Prediction : using Ensemble Learning

The Heart Disease Prediction Model utilizes Ensemble Learning, a powerful technique that combines multiple classifier models to enhance accuracy and robustness. It analyzes a set of input attributes, such as age, gender, chest pain type, resting blood pressure, serum cholesterol, and more, to predict the likelihood of heart disease. The models exceptional accuracy of 96% demonstrates its capability to make reliable predictions.

The project further provides a web application where users can conveniently input their attributes and receive predictions for heart disease. They can also explore and compare the results of the ensemble model with other selected classifiers, including Random Forest, Naïve Bayes, Logistic Regression, K-Nearest Neighbors, Decision Tree, Gradient Boosting, LightGBM, XGBoost, Multilayer Perceptron (Artificial Neural Network), and Support Vector Machine.

• Ensemble Learning: The model utilizes Ensemble Learning, combining multiple classifier models for improved accuracy and robust predictions.
• High Accuracy: The Heart Disease Prediction Model achieves an impressive accuracy of 96%, providing reliable results.
• Web Application: The project includes a user-friendly web application built with Streamlit, facilitating easy interaction with the prediction model.
• Model Comparison: Users can compare the ensemble models performance with a variety of other classifier models to assess its superiority.
• Input Attributes: Users can input essential attributes, including age, gender, chest pain type, and various health metrics, for personalized predictions.
• GitHub Repository: The project code and implementation are available in a GitHub repository, providing transparency and accessibility to the codebase.
• Dataset: The model is trained on the heart.csv dataset, containing crucial input features and corresponding target labels for heart disease prediction.
• Easy Installation: Users can effortlessly set up the web application locally by following the installation instructions.
• Contributions: The project welcomes contributions from the community, encouraging collaboration and improvements to the prediction model.