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Sathwika04/
Heart_Disease_Prediction

Name: Heart_Disease_Prediction
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❤️ Heart Disease Prediction App

A machine learning web app that predicts heart disease risk using patient health data. Built with Streamlit for the interface and scikit-learn for the models.

Overview

This project trains four different machine learning models on patient data, compares their performance, and uses the most accurate one to make predictions through an interactive web interface. Users can either:

Enter a single patient's details and get an instant prediction, or
Upload a CSV file to get predictions for many patients at once.

Models Used

Model	Description
Decision Tree	Tuned with `GridSearchCV` over depth, split, and leaf parameters
Logistic Regression	Best solver selected automatically from `lbfgs`, `liblinear`, `newton-cg`, `sag`, `saga`
Support Vector Machine (SVM)	Best kernel selected automatically from `linear`, `poly`, `rbf`, `sigmoid`
Random Forest	Tuned with `GridSearchCV`, currently the best-performing model

The app runs new input through all four models internally, and displays the final prediction from whichever model has the highest test accuracy.

Project Structure

.
├── app.py                    # Streamlit web app (3 tabs: Predict, Bulk Predict, Model Information)
├── decision.pkl               # Trains the Decision Tree model
├── logistic.pkl                # Trains the Logistic Regression model
├── support.pkl                # Trains the SVM model
├── random.pkl                # Trains the Random Forest model, scaler, and feature list
├── requirements.txt            # Python dependencies
├── sample_bulk_predict.csv     # Example CSV for testing the Bulk Predict tab
└── README.md

Note: The training scripts use a .pkl extension (despite being Python scripts) to match the original project naming. They are plain Python files and should be run with python <filename>.pkl.

Dataset

This project uses the Statlog Heart Disease dataset format, with the following columns:

Age, Sex, Chest pain type, BP, Cholesterol, FBS over 120, EKG results,
Max HR, Exercise angina, ST depression, Slope of ST,
Number of vessels fluro, Thallium, Heart Disease

The target column Heart Disease contains Presence / Absence labels, which are mapped to 1 / 0 during training.

The dataset file (cleaned_data.csv) is not included in this repository. Place your own copy in the project root before running the training scripts.

Setup

Clone the repository

bash
1git clone https://github.com/<your-username>/<your-repo>.git 2cd <your-repo>
Install dependencies

pip install -r requirements.txt
Add your dataset Place cleaned_data.csv in the project root.
Train the models Run each training script once. This generates the model files (.pkl) the app needs:

bash
1python decision.pkl 2python logistic.pkl 3python support.pkl 4python random.pkl
Run the app

streamlit run app.py

This opens the app in your browser at http://localhost:8501.

Feature Encoding Reference

Field	Encoding
Sex	`1` = Male, `0` = Female
Chest pain type	`1` = Typical Angina, `2` = Atypical Angina, `3` = Non-anginal Pain, `4` = Asymptomatic
FBS over 120	`1` = True, `0` = False
EKG results	`0` = Normal, `1` = ST-T Wave Abnormality, `2` = Left Ventricular Hypertrophy
Exercise angina	`1` = Yes, `0` = No
Slope of ST	`1` = Upsloping, `2` = Flat, `3` = Downsloping
Thallium	`3` = Normal, `6` = Fixed Defect, `7` = Reversible Defect

App Features

Predict tab — fill in a form with patient details and get an instant High Risk / Low Risk prediction with a confidence percentage.
Bulk Predict tab — upload a CSV of multiple patients and download predictions for all of them at once.
Model Information tab — view which model is currently used, its hyperparameters, and accuracy comparison across all four models.

Disclaimer

This app is for educational purposes only and is not a substitute for professional medical advice, diagnosis, or treatment. Always consult a qualified healthcare provider regarding any medical condition.

Sathwika04/Heart_Disease_Prediction