Note: The .pkl files are shared for academic project purposes and should only be loaded if the source is trusted.

LifeScanAI – Machine Learning Fueled Diagnostics

LifeScanAI is a machine learning–based web application designed to predict the risk of heart disease using user-provided health, lifestyle, and demographic data. The project integrates a trained ML model with a Flask backend and a clean, interactive frontend to provide instant risk assessment.

Problem Statement

Traditional heart disease risk assessment requires clinical tests and expert evaluation, which may not always be easily accessible. There is a need for a lightweight system that can provide preliminary risk analysis using available health indicators.

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Proposed Solution

LifeScanAI uses a supervised machine learning model trained on a large public health dataset to predict whether a person is at high or low risk of heart disease. Users input their health parameters through a web interface, and the system returns an instant prediction.

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Technologies Used

Machine Learning

• Python
• Pandas, NumPy
• Scikit-learn
• Logistic Regression
• StandardScaler

Backend

• Flask
• Flask-CORS
• Pickle (model serialization)

Frontend

• HTML5
• CSS3
• JavaScript (Fetch API)
• LocalStorage (for prediction history)

Deployment

• Backend: Deployed on Render
• Frontend: Deployed on Netlify

Project Structure

LifeScanAI/ │ ├── backend/ │ ├── app.py │ ├── heart_model.pkl │ ├── scaler.pkl │ └── requirements.txt │ ├── frontend/ │ ├── index.html │ ├── history.html │ ├── predict.html │ └── common-styles.css │ ├── training/ │ ├── train_model.ipynb │ └── heart_large.csv │ └── README.md

Model Details

Algorithm: Logistic Regression Train-Test Split: 80% Training / 20% Testing Task: Binary Classification (Heart Disease Risk) Output: High Risk of Heart Disease Low Risk of Heart Disease

Data Privacy Note

This application does not store user data on a server or database. Prediction history is stored locally in the browser using LocalStorage and can be cleared at any time.

⚠️ Disclaimer

This project is intended for educational and research purposes only. It is not a medical diagnosis tool and should not be used as a substitute for professional medical advice.

References

-> CDC Heart Disease Dataset -> Scikit-learn Documentation -> Flask Documentation