Ayu-Medical Diagnostic System

Brief Summary

The Medical Diagnostic System is an AI-driven application designed to predict various diseases with high accuracy. Using machine learning models, the system helps diagnose conditions like Diabetes, Breast Cancer, Kidney Disease, and Heart Disease. The models have been trained on relevant datasets and saved for deployment, enabling automated, efficient, and reliable medical predictions.

Model Accuracy

Disease	Model Type	Accuracy
Diabetes	Machine Learning Model	98.25%
Breast Cancer	Machine Learning Model	98.25%
Kidney Disease	Machine Learning Model	99%
Heart Disease	Machine Learning Model	85.25%

Technologies Used:

• Python
• Scikit-learn
• Pandas
• NumPy
• Jupyter Notebooks

Dataset Links

All the datasets were used from Kaggle:

• Diabetes Dataset
• Breast Cancer Dataset
• Heart Disease Dataset
• Kidney Disease Dataset

Project Structure

Medical_Diagnostic_Project/
│── model/               # Contains trained .pkl model files
│── notebook/            # Jupyter notebooks for training models
│── README.md            # Project documentation
│── requirements.txt     # Dependencies

Installation

Prerequisites

Ensure you have Python installed (>=3.8). Install dependencies using:

pip install -r requirements.txt

Usage

Training Models

To retrain any model, navigate to the notebook/ directory and execute the corresponding Jupyter notebook.

Predicting Diseases

Load the trained models from the model/ directory and use them to predict diseases:

import pickle
import numpy as np

# Load model
with open('model/diabetes_model.pkl', 'rb') as f:
    model = pickle.load(f)

# Example input features (replace with actual values)
input_data = np.array([[5, 116, 74, 0, 0, 25.6, 0.201, 30]])
prediction = model.predict(input_data)
print("Diabetes Prediction:", prediction)

References and Appendices

• Public medical datasets from Kaggle and UCI Machine Learning Repository
• Research papers on AI in healthcare diagnostics