Computational intelligence techniques for assessing anthropometric indices changes in female athletes

Kazemipoor, Mahnaz and Rezaeian, Mehdi and Kazemipoor, Maryam and Hamzah, Sareena Hanim and Shandilya, Shishir Kumar (2020) Computational intelligence techniques for assessing anthropometric indices changes in female athletes. Current Medical Imaging, 16 (4). pp. 288-295. ISSN 1573-4056, DOI https://doi.org/10.2174/1573405614666180905111814.

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Abstract

Background: Physical characteristics including body size and configuration, are considered as one of the key influences on the optimum performance in athletes. Despite several analyzing methods for modeling the slimming estimation in terms of reduction in anthropometric indices, there are still weaknesses of these models such as being very demanding including time taken for analysis and accuracy. Objectives: This research proposes a novel approach for determining the slimming effect of a herbal composition as a natural medicine for weight loss. Methods: To build an effective prediction model, a modern hybrid approach, merging adaptive-network-based fuzzy inference system and particle swarm optimization (ANFIS-PSO) was constructed for prediction of changes in anthropometric indices including waist circumference, waist to hip ratio, thigh circumference and mid-upper arm circumference, on female athletes after consumption of caraway extract during ninety days clinical trial. Results: The outcomes showed that caraway extract intake was effective on lowering all anthropometric indices in female athletes after ninety days trial. The results of analysis by ANFIS-PSO was more accurate compared to SPSS. Also, the efficiency of the proposed approach was confirmed using the existing data. Conclusion: It is concluded that a development in predictive accuracy and simplification capability could be attained by hybrid adaptive neuro-fuzzy techniques as modern approaches in detecting changes in body characteristics. These developed techniques could be more useful and valid than other conventional analytical methods for clinical applications.

Item Type: Article
Funders: UNSPECIFIED
Uncontrolled Keywords: Weight lowering activity; Human body configuration; Sports women; Artificial intelligence technique; Anti-obesity medicinal plants; Traditional complementary alternative medicine
Subjects: R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions: Faculty of Sports and Exercise Science (formerly known as Centre for Sports & Exercise Sciences)
Depositing User: Ms Zaharah Ramly
Date Deposited: 01 Jun 2023 08:20
Last Modified: 01 Jun 2023 08:20
URI: http://eprints.um.edu.my/id/eprint/37058

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