MediAI is a healthcare analytics and machine learning project designed to predict heart disease risk using patient health data. The project leverages a Random Forest Classification model to analyze medical attributes and classify patients into risk categories while providing confidence scores for each prediction.
The solution integrates data analytics, predictive modeling, and business intelligence visualization through an interactive Power BI dashboard, enabling healthcare professionals and analysts to better understand patient risk patterns and support data-driven decision-making.
- Predict heart disease risk using machine learning techniques.
- Identify high-risk and low-risk patient groups.
- Analyze healthcare trends across demographics and medical indicators.
- Evaluate model confidence and prediction reliability.
- Develop an interactive dashboard for healthcare analytics and reporting.
- Python
- Pandas
- NumPy
- Scikit-Learn
- Random Forest Classifier
- Power BI
- DAX
- CSV
- Pickle (.pkl)
The dataset contains patient medical information commonly used for heart disease assessment, including:
- Age
- Gender
- Chest Pain Type
- Resting Blood Pressure
- Cholesterol Level
- Fasting Blood Sugar
- Resting ECG Results
- Maximum Heart Rate
- Exercise-Induced Angina
- ST Depression
- Slope
- Number of Major Vessels
- Thalassemia
- Heart Disease Target Variable
- Data Cleaning
- Missing Value Verification
- Feature Selection
- Data Transformation
- Train-Test Split
- Random Forest Classification
- Model Evaluation
- Accuracy Score
- Confusion Matrix
- Classification Report
- Confidence Score Analysis
- Total Patients
- High-Risk Patients
- Low-Risk Patients
- Average Confidence Score
- Risk Distribution Analysis
- Risk by Gender
- Risk by Age Group
- Confidence by Chest Pain Type
- Average Confidence by Age Group
- Patient Risk Analysis Table
- Gender
- Age Group
- Risk Level
- Chest Pain Type
- Identified high-risk and low-risk patient populations using predictive analytics.
- Analyzed disease risk patterns across age groups and gender categories.
- Evaluated prediction confidence levels for healthcare decision support.
- Provided interactive healthcare intelligence through visual analytics.
- Supports early identification of potential heart disease cases.
- Enables data-driven healthcare decision-making.
- Assists healthcare professionals in patient risk assessment.
- Enhances healthcare analytics and reporting capabilities.
MediAI-AI-Powered-Healthcare-Risk-Prediction-Assistant/
│
├── MediAI_Analytics.ipynb
├── medi_ai_final.csv
├── medi_ai_model.pkl
├── medi_ai_dashboard.pbix
├── dashboard.png
├── README.md
└── requirements.txt
- Real-time patient risk prediction.
- Deployment using FastAPI or Streamlit.
- Explainable AI (XAI) integration.
- AI-powered healthcare recommendation engine.
- Healthcare chatbot integration.
Lubna Shireen R
Data Analytics | Machine Learning | Business Intelligence | Power BI
This project is licensed under the MIT License.