Decision support system for the prognostication of sarcopenia in adult women: Machine learning analysis using Korean National Health and Nutrition Examination Survey data
- Author:
Sae Mi LEE
1
;
Yeon Ju LEE
;
So Eun LEE
;
Ji Yeon SON
;
Ki-Jin RYU
;
Kwang Sik LEE
;
Tak KIM
;
Hyuntae PARK
Author Information
1. Department of Obstetrics and Gynecology, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
- From:Journal of Menopausal Medicine
2021;27(3):s12-
- CountryRepublic of Korea
- Language:English
-
Abstract:
Background:We used machine learning and population-based data for analyzing the determinants of sarcopenia in adult women and developing its decision support systems for various subgroups.
Methods:All data was acquired from the Korea National Health and Nutrition Examination Survey, and women 18 years and older were included in this research. The variables were selected based on female characteristics and the ability to be acquired in a survey format, and were ranked by importance using Random Forest. From this ranking, four main variables were selected, age, menopause age, menarche age and number of pregnancy. A decision supporting system was constructed based on a tree randomly selected from Random Forest.
Results:We defined sarcopenia as -2SD below the appendicular skeletal mass (ASM) index reference of 0.5136, and 89.87% (n = 8,610) were found non-sarcopenic and 10.13% (n = 971) were found sarcopenic. The subjects were divided into 6 groups based on menopausal status and BMI. The obese postmenopausal women had the highest number of sarcopenia, whereas the non-obese premenopausal women had the least number of sarcopenic subjects. In non-obese premenopausal women, which was considered to be at the lowest risk for sarcopenia, the most determining variable was the menarche age, followed by age and number of pregnancies. In obese and postmenopausal women, which was considered to be at the highest risk for sarcopenia, the most influential factor was the menopausal age, followed by age and menarche age.
Conclusions:We identified the major determinants of sarcopenia using machine learning and population-based data. This study demonstrated the strengths of the random forest as an effective decision support system for each stratified subgroup to find its own optimal cut-off points for the major variables of sarcopenia.
