Heart Disease Detection using Machine Learning
Qalandar Bux
🫀 Heart Disease Detection using Machine Learning
This project aims to predict the presence of heart disease using machine learning algorithms. It leverages a dataset of patient attributes and applies Decision Tree and K-Nearest Neighbors (KNN) classifiers to make predictions.
🚀 Project Overview
Heart disease is a leading cause of death worldwide. Early detection can help in timely treatment and potentially save lives. This project uses two supervised learning models to classify whether a person is likely to have heart disease based on medical features.
🛠️ Tools & Technologies
Python 3
scikit-learn
pandas
matplotlib (optional, for visualization)
Jupyter Notebook / VS Code
📂 Dataset
File:
heart.csvContains patient health records with features such as:
Age
Sex
Chest pain type
Resting blood pressure
Cholesterol level
Fasting blood sugar
Rest ECG
Maximum heart rate
Exercise-induced angina
ST depression
Number of major vessels
Thalassemia
Target (1: Disease, 0: No disease)
📊 Algorithms Used
✅ 1. Decision Tree Classifier
Simple and interpretable tree-based algorithm.
Suitable for datasets with both categorical and numerical features.
✅ 2. K-Nearest Neighbors (KNN)
Instance-based learning algorithm.
Classifies based on the majority vote from k nearest samples.
🧪 How to Run
Clone the Repository
Like this project
Posted Aug 30, 2025
Developed a heart disease prediction model using Decision Tree and KNN.
