Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases

TL;DR

This paper compares various machine learning algorithms for detecting cardiovascular diseases, emphasizing the effectiveness of ensemble methods and advanced models in improving diagnostic accuracy and reliability.

Contribution

It provides a comprehensive comparative analysis of multiple ML algorithms for CVD detection, highlighting the superior performance of ensemble methods and advanced algorithms.

Findings

Ensemble methods outperform individual algorithms in accuracy.

Gradient boosting and XGBoost show the highest reliability.

The study offers a framework adaptable for clinical implementation.

Abstract

The detection of cardiovascular diseases (CVD) using machine learning techniques represents a significant advancement in medical diagnostics, aiming to enhance early detection, accuracy, and efficiency. This study explores a comparative analysis of various machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. By utilising a structured workflow encompassing data collection, preprocessing, model selection and hyperparameter tuning, training, evaluation, and choice of the optimal model, this research addresses the critical need for improved diagnostic tools. The findings highlight the efficacy of ensemble methods and advanced algorithms in providing reliable predictions, thereby offering a comprehensive framework for CVD detection that can be readily implemented and adapted in clinical settings.