Disease Prediction Using Machine Learning

This project focuses on predicting the likelihood of diseases using machine learning algorithms. By analyzing patient data and identifying patterns, the model aims to assist in early diagnosis and treatment planning.

Table of Contents

• Introduction
• Features
• Datasets
• Requirements
• Installation
• Usage

Introduction

Accurate disease prediction is vital in healthcare for early intervention and improved patient outcomes. This project leverages machine learning techniques to analyze medical data and predict the presence or risk of diseases.

Features

• Data Preprocessing: Handling missing values, encoding categorical variables, and normalizing data.
• Feature Selection: Identifying the most relevant features for prediction.
• Model Training: Implementing various machine learning algorithms.
• Model Evaluation: Assessing performance using metrics like accuracy, precision, recall, and F1-score.
• Prediction: Providing disease risk predictions for new patient data.

Datasets

The project utilizes publicly available medical datasets. Ensure compliance with data usage policies and patient privacy regulations when using real-world data.

Requirements

• Python 3.7 or higher
• Jupyter Notebook
• Pandas
• NumPy
• scikit-learn
• Matplotlib
• Seaborn

Installation

1. Clone the Repository:

   git clone https://github.com/quangkmhd/Disease-Prediction-Using-Machine-Learning.git
   cd Disease-Prediction-Using-Machine-Learning

2. Create a Virtual Environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows, use 'venv\Scripts\activate'

3. Install Dependencies:

   pip install -r requirements.txt

Usage

1. Prepare the Dataset
2. Data Preprocessing
3. Feature Selection
4. Model Training
5. Model Evaluation
6. Prediction