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Project 1: AI-Based Diabetes Diagnosis Model

Overview

This project implements a machine learning model to predict the likelihood of diabetes based on input medical data. The AI model analyzes key health parameters like glucose levels, BMI, and age to provide a quick diagnosis. The system can be easily integrated into various applications for medical use.

Features

• AI-Powered Prediction: Leverages a pre-trained machine learning model to predict the risk of diabetes.
• Input Health Data: Users input basic health information such as glucose levels, BMI, age, and other medical factors.
• Accurate and Fast: Provides immediate and reliable results for early diagnosis of diabetes.

Technologies Used

• Machine Learning Model: Developed using popular machine learning libraries (such as Scikit-learn, TensorFlow, etc.).
• Input Data: Includes essential medical features like:
  • Glucose levels
  • Blood pressure
  • BMI (Body Mass Index)
  • Age
  • Number of pregnancies (for women)
  • Skin thickness
  • Insulin levels

Project Structure

Diabetes-Diagnosis-System/
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├── src/                # Source code for model implementation and prediction logic
├── model/              # Pre-trained machine learning model
├── data/               # Example datasets for testing and validation
├── docs/               # Documentation on how to use and modify the model
├── tests/              # Unit tests for model accuracy

Setup Instructions

1. Clone the Repository:

   git clone https://github.com/HoangTanIot/Project-1-AI
   cd Diabetes-Diagnosis-System

2. Install Dependencies:

   • Ensure that Python and the required libraries (like Scikit-learn, Pandas, NumPy, etc.) are installed.
   • Install dependencies using:

   pip install -r requirements.txt

3. Run the Model:

   • Use the provided script to input your data and get a diagnosis result.

   python predict_diabetes.py

4. Example Input: You will need to input the following parameters to get a prediction:

   • Glucose: 148
   • BMI: 33.6
   • Age: 50
   • [Other medical parameters]

   The system will output a prediction on whether the user is likely to have diabetes.

Model Description

The machine learning model is trained using the Pima Indians Diabetes Database and performs classification based on the provided features. The model uses logistic regression to classify whether the person is at risk of diabetes or not. The model can be retrained or replaced with more advanced models if needed.

Future Enhancements

• Add a web or mobile interface for easier input of data.
• Enhance the model by incorporating more features or more sophisticated algorithms like deep learning.

License

This project is licensed under the MIT License.

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