https://github.com/ruchit0807/heart_disease_prediction
An interactive ML-powered web app that predicts the risk of heart disease based on clinical inputs like age, chest pain, cholesterol, ECG, and more. Built using Python, Streamlit, and scikit-learn, it offers early risk assessment in a simple and accessible way—just enter your health metrics and get instant feedback.
https://github.com/ruchit0807/heart_disease_prediction
data-analysis data-science knn-regression pandas streamlit
Last synced: 2 months ago
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An interactive ML-powered web app that predicts the risk of heart disease based on clinical inputs like age, chest pain, cholesterol, ECG, and more. Built using Python, Streamlit, and scikit-learn, it offers early risk assessment in a simple and accessible way—just enter your health metrics and get instant feedback.
- Host: GitHub
- URL: https://github.com/ruchit0807/heart_disease_prediction
- Owner: Ruchit0807
- Created: 2025-06-22T09:21:53.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-22T09:54:26.000Z (about 1 year ago)
- Last Synced: 2025-06-22T10:27:05.751Z (about 1 year ago)
- Topics: data-analysis, data-science, knn-regression, pandas, streamlit
- Language: Python
- Homepage: https://heartdiseaseprediction-byruchit.streamlit.app/
- Size: 43.9 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 💓 Heart Disease Prediction App
## 🔗 APP LINK - https://heartdiseaseprediction-byruchit.streamlit.app/
🔍 "Know your heart before it breaks."
This smart and interactive web app predicts the likelihood of a heart attack based on key clinical and personal health indicators. Powered by machine learning, it offers an accessible tool for early risk assessment—just by entering a few health metrics.
🚀 What It Does
The app analyzes user input across several medically significant features to estimate the risk of heart disease. These inputs include:
🧓 Age
🚻 Sex
❤️ Chest Pain Type
🩺 Resting Blood Pressure (mm Hg)
🧪 Serum Cholesterol (mg/dl)
🍬 Fasting Blood Sugar > 120 mg/dl
🧠 Resting ECG Results
🏃 Maximum Heart Rate Achieved
🏋️ Exercise-Induced Angina
📈 Slope of ST Segment
## 🧠 Behind the Scenes
This app uses a trained machine learning model (KNN, Logistic Regression, etc.) on the Cleveland Heart Disease dataset to make predictions. It processes the provided inputs through a pre-fitted scaler and outputs the probability of heart disease risk in real-time.
## 👤 Who’s It For?
Anyone curious about their cardiovascular health! While not a replacement for a real diagnosis, it’s a helpful early warning tool for awareness and prevention.
## 💡 Technologies Used
🐍 Python
📊 scikit-learn, pandas
🌐 Streamlit (for UI)
💾 Joblib (for model deployment)