Disease Prediction Using Machine Learning

Overview

This project implements a machine learning-based disease prediction system that analyzes patient symptoms and medical history to predict the likelihood of various diseases. The objective is to aid early diagnosis and improve patient care by leveraging data-driven insights.

Features

• Predicts risk for multiple diseases using classifiers such as logistic regression, random forest, decision tree, Naive Bayes, and SVM.
• Takes user medical data and symptoms as input.
• Provides a confidence score for each prediction.
• Compares model performance via metrics like accuracy, confusion matrix, and precision-recall.
• Optionally provides home remedy suggestions and is not a substitute for professional medical advice.

Supported Diseases

• Diabetes
• Heart Disease
• Liver Disease
• Breast Cancer
• Kidney Disease
• Others (customize list based on datasets used)

Tech Stack

• Python 3.x
• Scikit-learn
• Pandas, NumPy, Matplotlib
• Web interface: Flask or Streamlit
• Jupyter Notebook for model exploration

Getting Started

Prerequisites

• Python 3.6 or higher
• pip

Installation

Clone the repository: git clone https://github.com/rajatkhurana08/Disease-Prediction-Using-Machine-Learning-and-Python.git

 cd disease-prediction-using-machine-learning

Author

Rajat Khurana   

Connect with me on LinkedIn: https://www.linkedin.com/in/rajatkhurana08/