The application of artificial neural networks in predicting blood pressure levels of youth archers by means of anthropometric indexes

Musa, Rabiu Muazu and Muhammad Zuhaili, Suhaimi and P. P. Abdul Majeed, Anwar and Mohamad Razali, Abdullah and Siti Musliha, Mat-Rasid and Mohd Hasnun Ariff, Hassan (2020) The application of artificial neural networks in predicting blood pressure levels of youth archers by means of anthropometric indexes. In: Lecture Notes in Bioengineering. Springer, Berlin, Germany, pp. 348-357. ISBN ISSN : 2195-271X

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Abstract

The present investigation aims at measuring as well as predicting blood pressure (BP) levels using anthropometric indexes. A standardised systolic blood pressure, (STBP) and diastolic blood pressure (DSBP) coupled with anthropometric evaluations of Body Mass Index waist to hip ratio, waist to height ratio, body fat percentage, and calf circumference was carried out on 50 youth archers. A Backward Regression Analysis (BRA) was used to determine the anthropometrics indexes that could predict both the STBP and DSBP whilst two models, namely Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) were developed based on the most correlated anthropometry. The BRA identified calf circumference (CC) as the highest correlated predictor for both STBP and DSBP. The ANN model developed demonstrated a better prediction efficacy against the MLR with an R2 as well as the mean absolute percentage error values of 0.95, 0.95, 0.050 and 0.06 as compared to MLR 0.26, 0.25, 8.46, 6.56 in the prediction of both the STBP and DSBP, respectively. It is evident from the present study that the BP levels of youth archers could be reliably measured using only their CC index.

Item Type: Book Chapter
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Anthropometrics indexes; Archery; Artificial Neural Networks; Blood pressure; Youth archers
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TS Manufactures
Faculty/Division: Faculty of Manufacturing and Mechatronic Engineering Technology
Faculty of Mechanical and Automotive Engineering Technology
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 02 Dec 2024 01:22
Last Modified: 02 Dec 2024 01:22
URI: http://umpir.ump.edu.my/id/eprint/42606
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