TY - GEN
T1 - Construction Formula for Biological Age Estimation using Principal Component Analysis in Imbalanced Dataset
AU - Zahra, Annisa
AU - Saputra, Angelica Patrica Djaya
AU - Sarwinda, Devvi
AU - Bustamam, Alhadi
AU - Barinda, Agian Jeffilano
AU - Marliau, Rheza Meida
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Aging is a natural process that leads to a decline in an individual's physical health condition. While chronological age (CA) only measures the length of time someone has lived since birth, biological age (BA) offers valuable insight into an individual's aging process. Consequently, BA proves to be a more effective indicator for evaluating the aging process than CA. Several methods have been developed to estimate BA. In this study, we employed Principal Component Analysis (PCA) to find important patterns of biomarkers in a dataset comprising 1245 individuals, specifically focusing on those within the age range of 30-65 and divided by gender. This study aims to utilized PCA for estimating the BA of the Indonesian population. PCA also allows for the assessment of the extent to which each biomarker contributes to the aging process. To address imbalanced dataset, the Synthetic Minority Oversampling Technique (SMOTE) was utilized. Biomarkers that truly influence BA will be selected using PCA. The relationship between CA and CBA shows at the value of coefficient of determination (r2) values, which were 0.51 for men and 0.46 for women. Furthermore, the fact that the mean of Corrected BA matches the mean of CA supports the model's precision in estimating biological age.
AB - Aging is a natural process that leads to a decline in an individual's physical health condition. While chronological age (CA) only measures the length of time someone has lived since birth, biological age (BA) offers valuable insight into an individual's aging process. Consequently, BA proves to be a more effective indicator for evaluating the aging process than CA. Several methods have been developed to estimate BA. In this study, we employed Principal Component Analysis (PCA) to find important patterns of biomarkers in a dataset comprising 1245 individuals, specifically focusing on those within the age range of 30-65 and divided by gender. This study aims to utilized PCA for estimating the BA of the Indonesian population. PCA also allows for the assessment of the extent to which each biomarker contributes to the aging process. To address imbalanced dataset, the Synthetic Minority Oversampling Technique (SMOTE) was utilized. Biomarkers that truly influence BA will be selected using PCA. The relationship between CA and CBA shows at the value of coefficient of determination (r2) values, which were 0.51 for men and 0.46 for women. Furthermore, the fact that the mean of Corrected BA matches the mean of CA supports the model's precision in estimating biological age.
KW - Aging
KW - Biological Age
KW - Health Index
KW - Principal Component Analysis
UR - http://www.scopus.com/inward/record.url?scp=85183466438&partnerID=8YFLogxK
U2 - 10.1109/ICIC60109.2023.10382096
DO - 10.1109/ICIC60109.2023.10382096
M3 - Conference contribution
AN - SCOPUS:85183466438
T3 - 2023 8th International Conference on Informatics and Computing, ICIC 2023
BT - 2023 8th International Conference on Informatics and Computing, ICIC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Informatics and Computing, ICIC 2023
Y2 - 8 December 2023 through 9 December 2023
ER -