Heart-Disease-Classification

Project Overview

This project focuses on utilizing machine learning to predict cardiovascular disease based on key risk factors. Cardiovascular disease is a leading global cause of preventable deaths, responsible for significant suffering and straining healthcare systems. It claims about 17.7 million lives annually, making up 44% of non-communicable disease fatalities.

We monitor risk factors such as blood pressure, obesity, age, gender, diet, exercise, smoking, insurance, mental and physical health, alcohol use, sleep, and health check-ups. Our aim is to leverage machine learning techniques for accurate disease prediction, contributing to research and prevention efforts. Below is an organized breakdown of the project's key components and features:

Data Preprocessing

Before diving into machine learning models, it's essential to preprocess the data to ensure its quality and suitability for analysis. The following steps have been taken:

1. Resampling Techniques:

   • Repeated Edited Nearest Neighbours (Undersampling)
   • Random Over Sampler (Oversampling)

2. Standardization

3. Feature Selection:

   • Recursive Feature Elimination

Machine Learning Models

Here are the models implemented:

1. Decision Tree Classifier

2. Logistic Regression

3. Support Vector Machine

Model Evaluation

To assess the performance of the machine learning models, the following evaluation metrics have been utilized:

1. Precision

2. Recall

3. F1-Score

Hyperparameter Tuning

Fine-tuning the model parameters is crucial for achieving optimal performance.

1. Grid Search