Predictive Modelling of Heart Disease: Exploring Machine Learning Classification Algorithms

Title

Predictive Modelling of Heart Disease: Exploring Machine Learning Classification Algorithms

Creator

Yadav J.; Nair A.M.; George J.; Alapatt B.P.

Description

In addressing the critical challenge of early and accurate heart failure diagnosis, this study explores the application of five machine learning models, including XGBoost, Decision Tree, Random Forest, Logistic Regression, and Gaussian Naive Bayes. Employing cross-validation and grid search techniques to enhance generalization, the comparative analysis reveals XGBoost as the standout performer, achieving a remarkable accuracy of 85%. The findings emphasize the significant potential of XGBoost in advancing heart failure diagnosis, paving the way for earlier intervention, and potentially improving patient prognosis. The study suggests that integrating XGBoost into diagnostic processes could represent a valuable and impactful advancement in the realm of heart failure prediction, offering promising avenues for improved healthcare outcomes. 2024 IEEE.

Source

International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Cross-validation; F1 Score; Grid Search; Heart attack prediction; Machine Learning; overfitting; Recall

Coverage

Yadav J., Christ (Deemed to Be University), Delhi NCR, India; Nair A.M., Christ (Deemed to Be University), Delhi NCR, India; George J., Christ (Deemed to Be University), Delhi NCR, India; Alapatt B.P., Christ (Deemed to Be University), Delhi NCR, India

Rights

Restricted Access

Relation

ISBN: 979-835038295-2

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICDCOT61034.2024.10515398

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193242794&doi=10.1109%2fICDCOT61034.2024.10515398&partnerID=40&md5=22f504b8b66edfbc39176235c31f6a3b