🫀 Heart Disease Prediction using Machine Learning 🤖

Introduction

This project uses machine learning to predict whether a patient has heart disease based on several clinical features such as age, sex, blood pressure, and cholesterol levels.

Multiple models were trained, including Random Forest, to provide a preliminary diagnosis using CSV or Excel patient data.

Main Goals

• Demonstrate a complete ML workflow (EDA, preprocessing, training, evaluation).
• Allow users to upload patient files and get instant predictions.
• Identify key features correlated with heart disease.

Dataset Features

• age – Age of the patient
• sex – Gender (0 = female, 1 = male)
• cp – Chest pain type
• trestbps – Resting blood pressure
• chol – Serum cholesterol
• fbs – Fasting blood sugar
• restecg – ECG results
• thalach – Max heart rate
• exang – Exercise-induced angina
• oldpeak – ST depression
• slope, ca, thal

Dataset Visualization

Model Performance

Random Forest Accuracy: 98.5%

Sample Output

Conclusion

This project demonstrates how machine learning can assist in early heart disease prediction. The model achieved high accuracy and can analyze new patient data effectively.

Author

Lilian Alhalabi