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K-means optimization with bat algorithm for predicting diabetes and hypertension risk in athletes’ comparison with machine learning.

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Název: K-means optimization with bat algorithm for predicting diabetes and hypertension risk in athletes’ comparison with machine learning.
Autoři: Sofro, A’yunin, Ariyanto, Danang, Prihanto, Junaidi Budi, Maulana, Dimas Avian, Romadhonia, Riska Wahyu, Maharani, Asri, Oktaviarina, Affi, Kurniawan, Ibnu Febry, Khikmah, Khusnia Nurul, Al Akbar, Muhammad Mahdy
Zdroj: International Journal of Advances in Intelligent Informatics; Nov2025, Vol. 11 Issue 4, p691-703, 13p
Témata: K-means clustering, MACHINE learning, HEALTH risk assessment, PREDICTIVE validity, OPTIMIZATION algorithms, HYPERTENSION risk factors, CLASSIFICATION, SPORTS statistics
Geografický termín: INDONESIA
Abstrakt: This research aims to develop an analytical approach to classification statistics. The proposed approach combines machine learning with optimization. Considering the urgency of research related to exploring the best methods to apply to sports data. This study proposes a novel framework that combines the k-means clustering results with the bat algorithm to optimize performance prediction for athletes in Indonesia. The proposed method aims to explore the data by comparing the classification performance of random forests, extremely randomized trees, and support vector machines. We conducted a case study using primary data from 200 respondents at Surabaya State University and the East Java National Sports Committee. The accuracy results in this study indicate that, based on the performance evaluation metric, the best approach is random forest clustering using k-means with bat algorithm optimization, achieving 81.25% accuracy, compared with other machine learning approaches. This research contributes to the field of classification statistics by introducing a novel hybrid framework that integrates machine learning, clustering, and optimization techniques to improve predictive accuracy, particularly in sports analytics. Beyond sports science, the proposed approach can be adapted to other domains that require robust performance prediction and decision support, such as health analytics, educational assessment, and human resource selection. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:This research aims to develop an analytical approach to classification statistics. The proposed approach combines machine learning with optimization. Considering the urgency of research related to exploring the best methods to apply to sports data. This study proposes a novel framework that combines the k-means clustering results with the bat algorithm to optimize performance prediction for athletes in Indonesia. The proposed method aims to explore the data by comparing the classification performance of random forests, extremely randomized trees, and support vector machines. We conducted a case study using primary data from 200 respondents at Surabaya State University and the East Java National Sports Committee. The accuracy results in this study indicate that, based on the performance evaluation metric, the best approach is random forest clustering using k-means with bat algorithm optimization, achieving 81.25% accuracy, compared with other machine learning approaches. This research contributes to the field of classification statistics by introducing a novel hybrid framework that integrates machine learning, clustering, and optimization techniques to improve predictive accuracy, particularly in sports analytics. Beyond sports science, the proposed approach can be adapted to other domains that require robust performance prediction and decision support, such as health analytics, educational assessment, and human resource selection. [ABSTRACT FROM AUTHOR]
ISSN:24426571
DOI:10.26555/ijain.v11i4.1816