Experimental Analysis of Machine Learning Algorithms for Heart Disease Prediction

DOI

10.2991/978-94-6463-738-0_101How to use a DOI?

Keywords

Heart Disease Prediction; Machine Learning; Boosting Algorithms; XGBoost; Adaboost; State-of-the-Art Models; Healthcare Analytics; Classification Models; Medical Diagnosis

Abstract

Cardiovascular disease continues to be a primary cause of death globally, highlighting the need for the creation of precise and effective prediction models for early detection. This research assesses the efficacy of multiple machine learning algorithms, including Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Adaboost, XGBoost, and state-of-the-art (SOTA) models across three publicly accessible heart disease datasets: the Cleveland Dataset, the Heart Failure Clinical Records Dataset, and the Cardiovascular Heart Disease Dataset. The models are evaluated on accuracy, precision, recall, and macro-F1 score to ascertain their efficacy in predicting heart disease. The findings indicate that boosting-based models, specifically XGBoost and Adaboost, surpass conventional classifiers in all assessment measures. The state-of-the-art models have superior predictive power, underscoring the need of sophisticated techniques in healthcare analytics. The work underscores the capability of machine learning to augment cardiac disease identification, therefore facilitating enhanced clinical decision-making and patient outcomes.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Anand Tamrakar
AU  - Aamir Khan
PY  - 2025
DA  - 2025/06/22
TI  - Experimental Analysis of Machine Learning Algorithms for Heart Disease Prediction
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 1307
EP  - 1320
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_101
DO  - 10.2991/978-94-6463-738-0_101
ID  - Tamrakar2025
ER  -