Health Risk Prediction System

Overview

The Health Risk Prediction System is a machine learning application built to predict health risks based on various body measurements. It classifies an individual's risk level for diseases such as Heart Disease, Diabetes, Osteoporosis, etc., based on physical parameters such as body fat percentage, age, weight, and more.

This project leverages a Decision Tree Classifier model trained on historical health data, which allows users to enter their details (e.g., body fat, age, weight, etc.) and receive a prediction of their health risk.

Features

• User Input Interface: A web-based application built with Streamlit where users can input their body measurements and receive predictions.
• Disease Classification: Based on input data, the system predicts the risk level for various diseases such as Heart Disease, Diabetes, Osteoporosis, etc.
• Preprocessing: The system applies necessary transformations including scaling and label encoding to prepare the data for prediction.
• Model Performance: The decision tree model is fine-tuned using cross-validation and hyperparameter tuning to ensure high accuracy.

Technologies Used

• Python: Core programming language.
• Streamlit: Framework for building the interactive web interface.
• scikit-learn: Machine learning library for training the Decision Tree Classifier model.
• Pandas: Data manipulation and preprocessing.
• Joblib: Used for saving and loading the trained model and other preprocessed objects.

Installation

1. Clone the Repository:

   git clone https://github.com/maqeel019/DiseasePredicationModel/.git

2. Install Dependencies:

   Ensure that you have pip installed, and then run the following to install required libraries:

   pip install -r requirements.txt

3. Run the Application:

   After installing the dependencies, you can run the Streamlit application using:

   streamlit run app.py

   This will open a local web server where you can interact with the app.

How It Works

1. Data Input: The user is prompted to enter their personal details such as body fat percentage, age, weight, height, etc.
2. Data Preprocessing: The input data is preprocessed, including scaling continuous variables and encoding categorical variables.
3. Prediction: The processed data is passed through the trained Decision Tree model, which classifies the risk level for various diseases.
4. Result: The predicted disease risk (e.g., Heart Disease - High Risk) is displayed to the user.

Files in the Repository

• app.py: Streamlit application file where the UI is implemented.
• model.py: Python file containing the code for training the Decision Tree model.
• health_data.csv: Example dataset used for training the model.
• decision_tree_model.joblib: Saved model for disease prediction.
• disease_encoder.joblib: Saved label encoder for converting disease labels.
• scaler.joblib: Saved scaler used for scaling input data.
• requirements.txt: A file listing the required Python packages for the project.

Model Training

• The model is trained using a Decision Tree Classifier, and the data is preprocessed to handle missing values and scale continuous features.
• Hyperparameter tuning is done using GridSearchCV to find the optimal model parameters.

Example Input

Here’s an example of the input fields that a user will be required to fill out:

• Density: 1.1
• Body Fat (%): 18.7
• Age: 45
• Weight (kg): 75
• Height (cm): 180
• Neck (cm): 37
• Chest (cm): 102
• Abdomen (cm): 90
• Hip (cm): 98
• Thigh (cm): 65
• Knee (cm): 43
• Ankle (cm): 22
• Biceps (cm): 36
• Forearm (cm): 30
• Wrist (cm): 18
• Gender: Female

Example Output

After the user provides the inputs and clicks Predict, the model will predict the disease risk level, such as:

Predicted Disease Class: No Significant Risk

Contributing

Feel free to contribute by submitting a pull request with any improvements or suggestions. You can also open an issue for bugs or feature requests.