Hybrid genetic algorithms-driven optimization of machine learning models for heart disease prediction.

Sherko H Murad, Noor Bahjat Tayfor, Nozad H Mahmood, Lawson Arman

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Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Sherko H MuradComputer Science Department, Cihan University of Sulaimaniya, Sulaymaniyah, Kurdistan, Iraq.

Noor Bahjat TayforInstitute of Informatic, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary.

Nozad H MahmoodCihan University Sulaimaniya Research Center (CUSRC), Cihan University Sulaimaniya, Sulaymaniyah City, Kurdistan Region, Iraq.

Lawson ArmanDepartment of facility Therapy named after professor. V.A. Valdman, St Peterburg state Pediatric Medical university, Saint Petersburg, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning (ML) models, such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), play a vital role in predicting heart disease. However, their performance is often limited by poor hyperparameter selection. This study presents a novel hybrid approach that uses a Genetic Algorithm (GA) to systematically optimize the hyperparameters of KNN and SVM models, leading to improved classification outcomes. The optimization driven by the GA resulted in significant performance improvements, increasing the classification accuracy of KNN to 95.38 % and SVM to 90 %. Furthermore, there were significant improvements in precision, recall, and F-score. Our findings demonstrate that GA-based hyperparameter tuning is an effective strategy for improving the predictive power and clinical relevance of ML models used for heart disease classification.•

Indexed as

Cardiovascular disease predictionGenetic algorithm (GA)K-nearest neighbour (KNN)Machine learning (ML)Support vector machine (SVM)

Identifiers

PMID40735521

DOI10.1016/j.mex.2025.103510

PMCPMC12305601

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Textmetadata

LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.