Chronic Disease Prediction System

Overview

This project is a Chronic Disease Prediction System that uses machine learning to assess the likelihood of a person being at risk of various chronic diseases, such as diabetes, hypertension, and cardiovascular diseases. The model takes inputs such as personal medical history, family history, biometric data, and lifestyle factors to provide an estimated risk percentage.

Features:

• Prediction for multiple chronic diseases: Currently supports the prediction of diabetes but is designed to expand to other diseases.
• User-friendly interaction: The user provides their health data step by step through clear and interactive questions.
• Probability-based risk assessment: Displays a detailed probability percentage of the user's risk, with adjustments based on their input.
• Adjustable confidence levels: Risk levels are dynamically adjusted using a step-wise method to provide more realistic interpretations.

Technologies Used

• Python 3.x
• Scikit-learn: For model training and prediction.
• Pandas & Numpy: For data handling and manipulation.
• Joblib: For saving and loading machine learning models.

Getting Started

Prerequisites

To run this project locally, ensure you have Python 3.x installed and the following packages:

  pip install numpy pandas scikit-learn joblib

Installing

Clone the repository to your local machine:

 git clone https://github.com/your-username/chronic-disease-prediction.git
cd chronic-disease-prediction

Load your saved machine learning model and scaler inside the models/ directory. If these models are not available, train and save them using a chronic disease dataset.

Run the prediction script:

 python main.py

How It Works

Upon running the script, users will be prompted to provide health-related inputs such as:

Number of pregnancies (for female users),

Glucose level

Blood pressure

Skin fold thickness

BMI (calculated using height and weight)

Family history of chronic diseases

Age.

After collecting the inputs, the model will estimate the user's risk of developing chronic diseases based on the data provided. It will return a probability percentage that indicates the likelihood of the user being at risk.

Adjusting Predictions

The system employs an adjustment mechanism to provide realistic probability estimates:

For high initial probabilities (≥ 80%), a small increase is added. For lower probabilities, a larger adjustment is made to reflect real-world risk factors. Example Output bash Copy code How many times have you been pregnant? (if not applicable, enter 0): 2 What is your fasting glucose level? (mg/dL): 105 What is your diastolic blood pressure? (mmHg): 85 ... After adjustment, there is a 67.32% chance that you are at risk of chronic disease. The model predicts that you are at risk of chronic disease. Customization The project is designed to be expanded for other chronic diseases beyond diabetes. To support a new disease:

Collect relevant data for that disease (e.g., cholesterol levels for heart disease). Update the input questions to reflect the disease's specific risk factors. Train a new machine learning model or retrain the existing model to predict the new chronic disease. Model Training For users who want to retrain the model for another disease or update the existing one:

Use a chronic disease dataset in CSV format. Train the model using scikit-learn with the features specific to your disease. Save the model and the scaler in the models/ directory using joblib. python Copy code from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler import joblib

Assuming data is loaded into X_train, y_train

scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train)

model = LogisticRegression() model.fit(X_train_scaled, y_train)

Save the model and scaler

joblib.dump(model, 'models/chronic_disease_model.pkl') joblib.dump(scaler, 'models/scaler.pkl') Future Improvements Support for other diseases: Expand prediction to other chronic diseases such as hypertension and cardiovascular diseases. Web-based Interface: Integrate with a web front-end to improve user accessibility. Mobile App: Build a mobile app for easy input and prediction. Contributing We welcome contributions! Feel free to submit a pull request or report an issue.

Fork the repository. Create your feature branch (git checkout -b feature/new-disease). Commit your changes (git commit -m 'Add new disease prediction'). Push to the branch (git push origin feature/new-disease). Open a pull request. License This project is licensed under the MIT License - see the LICENSE file for details.

This README.md provides a clear overview of the project, instructions for setup,