A hybrid feature selection algorithm using simplified swarm optimization for body fat prediction. (November 2022)

• Record Type: Journal Article
• Authors: Lai, Chyh-Ming; Chiu, Chun-Chih; Shih, Yuh-Chuan; Huang, Hsin-Ping
• Is Part Of: Computer methods and programs in biomedicine. Volume 226(2022)
• Publication Date: 2022-11
• Subjects: Body fat prediction -- Multi-filter ensemble technique -- Wrapper -- Simplified swarm optimization -- Feature selection -- Multiple linear regression; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107183
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An improved, simplified swarm optimization was proposed for body fat prediction. A biased random initialization scheme was introduced to create population diversity at the solution level. A feature pruning scheme based on the significant effect was proposed to enhance the exploitation of SSO. Extensive experiments on nine anthropometric datasets were conducted. Abstract: Background and objectives: Obesity is one of the chronic diseases that seriously threaten people's health outcomes globally. Since the prevalence of obesity is increasing among people of all ages, measuring the body fat percentages is vital before treatment. However, the body fat percentage cannot be accurately measured by weighing. While many devices are commonly used to measure the body fat percentage, these devices are expensive and depend on complex instruments. Therefore, more practical and cost-effective solutions are desired to measure body fat accurately. This study presents a hybrid feature selection method based on a VIKOR-based multi-filter ensemble technique (VMFET) and an improved simplified swarm optimization (iSSO) to predict the body fat percentage with low prediction error. Methods: The study followed a two-phase process. First, VMFET was used to aggregate the statistical outcomesof individual filters to filter the most informative features from the original dataset. Then, the selected features are applied to the next phase. Second, iSSO was tailored with a biased random initialization scheme, effect-based feature pruning scheme, and multiple linear regression as a wrapper method to improve the prediction performance and select the optimal feature subset. Studies results: Extensive experiments were performed using nine datasets to verify the performance of the proposed method empirically, and the corresponding results were compared with up-to-date studies. Conclusion: The statistical results demonstrated that the proposed method offers a promising and effective tool for predicting body fat. Significance: The hybrid feature selection model can enhance prediction accuracy and lower prediction error.