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International Journal of Sports Technology, 2023, 4(1); doi: 10.38007/IJST.2023.040105.

Prediction Model of the Influence of Physical Training on the Percentage of Body Fat of College Students Based on Artificial Neural Network


Jie Ren

Corresponding Author:
Jie Ren

College of Physical Education and Training, Harbin Sports University, Harbin, China


In the past ten years, the health level of college students has declined significantly. Regular exercise is a good living habit, which can reduce the risk of heart disease, obesity and related mental health. The purpose of this study is to use artificial neural network to study the change trend of college students’ body fat percentage in physical training. The results showed that, during the period of 1-8 months, the percentage of body fat of male and female students selected in the experiment gradually declined after physical training. At the early stage of training, the percentage of body fat of males was close to 0.21 and declined slowly, while as the physical training continued, the decline rate gradually increased, and finally became slow and gradually close to 0.11 indefinitely. The percentage of body fat of female students also showed the same trend, with an initial BMI of about 0.35, gradually approaching 0.21. Then the percentage of body fat of college students who had undergone 36-43 months of physical training was studied and analyzed. This paper found that the percentage of body fat of college students turned again when it was gradually close to the normal value, and there was a growing trend. Males gradually showed an upward trend when it dropped to 0.11, and the growth rate was small, while females gradually increased from about 0.2. Based on the experimental results of analyzing the percentage of body fat of college students using artificial neural network technology, firstly, physical training is very effective for reducing the percentage of body fat of college students, but meanwhile, students should pay attention to the intensity and duration of exercise. Secondly, it is more important to monitor the health condition regularly.


Percentage of Body Fat, Physical Training, Artificial Neural Network, SVM Algorithm

Cite This Paper

Jie Ren. Prediction Model of the Influence of Physical Training on the Percentage of Body Fat of College Students Based on Artificial Neural Network. International Journal of Sports Technology (2023), Vol. 4, Issue 1: 63-75. https://doi.org/10.38007/IJST.2023.040105.


[1] Harmouche Karaki Mireille. Combined effect of physical activity and sedentary behavior on body composition in university students. Clinical Nutrition. (2020) 39(5): 1517-1524. https://doi.org/10.1016/j.clnu.2019.06.015

[2] Lowe Michael R. Weight suppression uniquely predicts body fat gain in first-year female college students. Eating behaviors. (2019) 32(1): 60-64. https://doi.org/10.1016/j.eatbeh.2018.11.005

[3] Ji-Woon Kim. Effect of circuit training on body composition, physical fitness, and metabolic syndrome risk factors in obese female college students. Journal of exercise rehabilitation. (2018) 14(3): 460-460. https://doi.org/10.12965/jer.1836194.097

[4] Ojo Gideon, Olamide Adetola. The relationship between skinfold thickness and body mass index in estimating body fat percentage on Bowen University students. International Biological and Biomedical Journal. (2017) 3(3): 138-144.

[5] Mill Ferreyra E. Estimation of the percentage of body fat based on the body mass index and the abdominal circumference: Palafolls Formula. Semergen. 45.2 (2018) 45(2): 101-108.

[6] Ramirez-Velez Robinson. Body adiposity index performance in estimating body fat percentage in Colombian college students: Findings from the FUPRECOL-adults study. Nutrients. (2017) 9(1): 40-40. https://doi.org/10.3390/nu9010040

[7] Ashtary-Larky Damoon. Effects of resistance training combined with a ketogenic diet on body composition: a systematic review and meta-analysis. Critical Reviews in Food Science and Nutrition. (2022) 62(21): 5717-5732. https://doi.org/10.1080/10408398.2021.1890689

[8] Le Thu Trang, Nguyen Nam Trung, Dinh-Toi Chu, Nguyen Thi Hong Hanh. Percentage body fat is as a good indicator for determining adolescents who are overweight or obese: a cross-sectional study in Vietnam. Osong public health and research perspectives. (2019) 10(2): 108-108. https://doi.org/10.24171/j.phrp.2019.10.2.10

[9] Ul Ha, Ijaz. A comparative study of nutritional status, knowledge attitude and practices (KAP) and dietary intake between international and Chinese students in Nanjing, China. International journal of environmental research and public health. (2018) 15(9): 1910-1910. https://doi.org/10.3390/ijerph15091910

[10] Yehong Yang. Sleeping time, BMI, and body fat in Chinese freshmen and their interrelation. Obesity Facts. (2020) 2(2): 179-190. https://doi.org/10.1159/000506078

[11] Abiodun Oludare Isaac. State-of-the-art in artificial neural network applications: A survey. Heliyon. (2018) 4(11): e00938-e00938. https://doi.org/10.1016/j.heliyon.2018.e00938

[12] Gamahara Masataka, Yuji Hattori. Searching for turbulence models by artificial neural network. Physical Review Fluids. (2017) 2(5): 054604-054604. https://doi.org/10.1103/PhysRevFluids.2.054604

[13] Sharma Sagar, Simone Sharma, Anidhya Athaiya. Activation functions in neural networks. Towards data science. (2017) 6(12): 310-316. https://doi.org/10.33564/IJEAST.2020.v04i12.054

[14] Cai, Zi, Jinguo Liu. Approximating quantum many-body wave functions using artificial neural networks. Physical Review B. (2018) 97(3): 035116-035116. https://doi.org/10.1103/PhysRevB.97.035116

[15] Anitescu Cosmin. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials and Continua. (2019) 59(1): 345-359. https://doi.org/10.32604/cmc.2019.06641

[16] Admasie Samuel. Intelligent islanding detection of multi-distributed generation using artificial neural network based on intrinsic mode function feature. Journal of Modern Power Systems and Clean Energy. (2020) 8(3): 511-520. https://doi.org/10.35833/MPCE.2019.000255

[17] Ferri-Morales Asuncion. Agreement between standard body composition methods to estimate percentage of body fat in young male athletes. Pediatric exercise science. (2018) 30(3): 402-410. https://doi.org/10.1123/pes.2017-0171

[18] Burns Ryan D., You Fu, Nora Constantino. Measurement agreement in percent body fat estimates among laboratory and field assessments in college students: Use of equivalence testing. PloS one. (2019) 14(3): e0214029-e0214029. https://doi.org/10.1371/journal.pone.0214029

[19] Pin-Hao Andy Chen, Robert S. Chavez, Todd F. Heatherton. Structural integrity between executive control and reward regions of the brain predicts body fat percentage in chronic dieters. Cognitive neuroscience. (2017) 8(3): 162-166. https://doi.org/10.1080/17588928.2016.1235556

[20] Siregar, Sandy Putra, Anjar Wanto. Analysis of artificial neural network accuracy using backpropagation algorithm in predicting process (forecasting). IJISTECH (International Journal of Information System and Technology). (2017) 1(1): 34-42. https://doi.org/10.30645/ijistech.v1i1.4