suruchiksd / Symptom-to-Disease-Predictor

This project is designed to predict diseases using machine learning techniques. It operates by analyzing user-reported symptoms to diagnose possible medical conditions. The system employs three machine learning classifiers—Decision Tree, Random Forest, and Naive Bayes—to process and interpret the data.

Disease Predictor Using Machine Learning

Overview

This project is a disease prediction system that uses machine learning algorithms to diagnose diseases based on user-reported symptoms. The system integrates three types of classifiers—Decision Tree, Random Forest, and Naive Bayes—into a Tkinter-based graphical user interface (GUI) to provide predictions.

Features

• Disease Prediction: Predicts potential diseases based on user input symptoms.
• Machine Learning Models: Utilizes Decision Tree, Random Forest, and Naive Bayes classifiers.
• User Interface: Interactive GUI built with Tkinter for ease of use.
• Data Handling: Handles medical datasets to train and test the prediction models.

Getting Started

Prerequisites

Ensure you have the following installed:

• Python 3.x
• Required Python libraries

Installation

1. Clone the Repository:

   git clone https://github.com/suruchiksd/Symptom-to-Disease-Predictor.git
   cd Symptom-to-Disease-Predictor
   

2. Install Dependencies:

   pip install -r requirements.txt
   

Running the Application

python main.py