🏥 AI-Based Multiple Disease Detection System

An intelligent healthcare prediction system built using Machine Learning (Random Forest) that predicts diseases based on selected symptoms and provides precautionary recommendations with confidence percentage.

📌 Project Overview

This project is designed to assist in early disease detection using symptom-based prediction. The system uses a trained machine learning model to analyze user-selected symptoms and predict the most probable disease.

It also provides:

Prediction confidence percentage

Basic precaution suggestions

User-friendly GUI interface

🎯 Features

✔ Multiple disease prediction ✔ Random Forest Machine Learning model ✔ Checkbox-based symptom selection ✔ Prediction confidence (%) ✔ Precaution recommendations ✔ Professional GUI using Tkinter ✔ Windows + Python 3.12 compatible

🧠 How It Works

User selects symptoms from GUI.

Symptoms are converted into numerical input.

The trained Random Forest model predicts the disease.

The system displays:

Predicted Disease

Confidence Score

Recommended Precautions

🛠 Technologies Used

Python

Pandas

NumPy

Scikit-learn

Tkinter (GUI)

Machine Learning (Random Forest Classifier)

📊 Dataset

The model is trained using a publicly available symptom-disease dataset from Kaggle.

Dataset contains:

130+ symptoms

40+ diseases

Binary symptom encoding (0/1)