Open Access

ARTICLE

Optimized Cardiovascular Disease Prediction Using Clustered Butterfly Algorithm

Kamepalli S. L. Prasanna¹, Vijaya J², Parvathaneni Naga Srinivasu¹, Babar Shah³, Farman Ali^4,*

1 Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati, 522503, Andhra Pradesh, India
2 Department of Data Science and Artificial Intelligence, International Institute of Information Technology, Naya Raipur, 493661, Chhattisgarh, India
3 College of Technological Innovation, Zayed University, Dubai, 19282, United Arab Emirates
4 Department of Applied AI, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, 03063, Republic of Korea

* Corresponding Author: Farman Ali. Email:

(This article belongs to the Special Issue: Recent Advancements in Machine Learning and Data Analysis for Disease Detection)

Computers, Materials & Continua 2025, 85(1), 1603-1630. https://doi.org/10.32604/cmc.2025.068707

Received 04 June 2025; Accepted 10 July 2025; Issue published 29 August 2025

Abstract

Cardiovascular disease prediction is a significant area of research in healthcare management systems (HMS). We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance. The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data. We present Clustered Butterfly Optimization Techniques (RoughK-means+BOA) as a new hybrid method for predicting heart disease. This method comprises two phases: clustering data using Roughk-means (RKM) and data analysis using the butterfly optimization algorithm (BOA). The benchmark dataset from the UCI repository is used for our experiments. The experiments are divided into three sets: the first set involves the RKM clustering technique, the next set evaluates the classification outcomes, and the last set validates the performance of the proposed hybrid model. The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97. This result is comparatively better than other combinations of optimization techniques. In addition, this approach effectively enhances data segmentation, optimization, and classification performance.

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Keywords

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