Journal of Angiotherapy
Angiogenesis, Inflammation & Therapeutics | Online ISSN  2207-872X
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Contents Vol 8 (3)

Wild Horse Optimizer and Support Vector Machine (SVM) Classifier Predicts The Heart Disease Converging Nature-Motivated Optimization and Machine Learning

Vishwanadham Mandala 1*, Srinivas Naveen Dolu Surabhi 2, V. R. Balaji 3, Dipak Raghunath Patil 4, Saiyed Faiayaz Waris 5, G. Shobana 6

+ Author Affiliations

Journal of Angiotherapy 8 (3) 1-12 https://doi.org/10.25163/angiotherapy.839535

Submitted: 08 January 2024 Revised: 28 February 2024  Published: 03 March 2024 

Abstract

Background: Heart disease is one of the most known and deadly diseases in the world and many people lose their lives from this disease every year. Early detection of this disease is vital to save people’s lives. Machine Learning (ML), an artificial intelligence technology, is one of the most convenient, fastest, and low-cost ways to detect disease. Methods: This research work, presented a Wild Horse Optimizer (WHO) based feature selection and Support Vector Machine (SVM) developed a classifier for the forecasting of data related to heart diseases. The WHO algorithm that draws inspiration from the social behaviours of wild horses is presented in this work. Horses typically reside in groups consisting of a stallion, numerous mares, and young foals. Horses can be seen engaging in a variety of behaviours, including leading, grazing, chasing, and mating. The interesting quality that sets horses apart from other animals is their kindness. When a horse is decent, before they reach maturity, its foals break away from the herd and join different groups. To avoid the father mating with the siblings or daughter, this separation occurred. The horse's decent behavior served as the primary source of inspiration for the suggested algorithm. Discussion: The models were created by using several ML techniques to train the feature-selected Cleveland heart disease dataset were evaluated and their results were compared. The parameters like Sensitivity, Accuracy, Specificity, and Area under Curve of the SVM classifier model are trained on the dataset utilizing the WHO approach which yields better results when compared with the other existing approaches. Conclusion: According to the findings, the wild horse optimization algorithm and SVM classifier combo performs best when used to forecast heart disease.

Keyword: Machine Learning, Wild Horse Optimization Algorithm (WHO), Support Vector Machine (SVM).

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