Heart Disease Prediction Using Machine Learning

Project Synopsis & Overview

This project develops an efficient heart disease prediction system using machine learning techniques. The system uses healthcare patient data containing relevant medical attributes for predicting the likelihood of heart disease. The collected data is preprocessed to handle missing values, noise, and unsuitable data formats before model training. Exploratory Data Analysis is performed to understand the characteristics and relationships within the dataset using different visualization techniques. Multiple machine learning algorithms, including K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, and XGBoost, are trained and evaluated for heart disease prediction. The dataset is divided into training and testing sets, and model performance is evaluated using classification accuracy, recall, cross-validation, and confusion matrix analysis. The algorithms are compared to identify the model that provides suitable prediction performance. The trained classifier can then be used to predict the possibility of heart disease based on patient information. The system aims to support early identification of heart disease and provide data-driven assistance for timely medical decision-making.

Complete Technical Specifications