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This study applies five machine learning approaches to estimate T-score of bone mineral density and compares predictive accuracy versus multiple linear regression while ranking 25 risk 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\nComparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort  \nShiow-Jyu Tzoua,b, Chung-Hsin Pengc, Li-Ying Huangd, Fang-Yu Chend, Chun-Heng Kuod, Chung-Ze Wue, Ta-Wei Chuf,g,*  \naTeaching and Researching Center, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan, ROC; bInstitute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC; cDepartment of Urology, Cardinal Tien Hospital, School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan, ROC; dDivision of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei, Taiwan; eDepartment of Internal Medicine, Shuang Ho Hospital, New Taipei City, Division of Endocrinology and Metabolism, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan, ROC; fDepartment of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC; gMJ Health Research Foundation, Taipei, Taiwan, ROC  \nAbstract  \nBackground: Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of risk factors for osteoporosis were conducted using traditional statistical methods, but more recent efforts have turned to machine learning approaches. Most such efforts, however, treat the target variable (bone mineral density [BMD] or fracture rate) as a categorical one, which provides no quantitative information. The present study uses five different machine learning methods to analyze the risk factors for T-score of BMD, seeking to (1) compare the prediction accuracy between different machine learning methods and traditional multiple linear regression (MLR) and (2) rank the importance of 25 different risk factors.  \nMethods: The study sample includes 24 412 women older than 55 years with 25 related variables, applying traditional MLR and five different machine learning methods: classification and regression tree, Naïve Bayes, random forest, stochastic gradient boosting, and eXtreme gradient boosting. The metrics used for model performance comparisons are the symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error.  \nResults: Machine learning approaches outperformed MLR for all four prediction errors. The average importance ranking of each factor generated by the machine learning methods indicates that age is the most important factor determining T-score, followed by estimated glomerular filtration rate (eGFR), body mass index (BMI), uric acid (UA), and education level.  \nConclusion: In a group of women older than 55 years, we demonstrated that machine learning methods provide superior performance in estimating T-Score, with age being the most important impact factor, followed by eGFR, BMI, UA, and education level. Keywords: Chinese; Machine learning; Osteoporosis  \n1. INTRODUCTION  \nReduced birth rates and increased life expectancy have driven population aging in the developed world, but these trends in recent years are increasingly apparent in the developing world as  \n* Address correspondence. Dr. Ta-Wei Chu, Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, 325, Section, 2, Chenggong Road, Taipei 114, Taiwan, ROC. E-mail address:  \n[taweichu@gmail.com](taweichu@gmail.com) (T.-W. Chu).  \nConflicts of interest: The authors declare that they have no conflicts of interest related to the subject matter or materials discussed in this article.  \nJournal of Chinese Medical Association. (2023) 86: 1028-1036.  \nReceived April 17, 2023; accepted July 3, 2023. doi: 10. 1097/JCMA.0000000000000999  \nCopyright © 2023, the Chine","cbCaiohag6ao06mA","https://ap.wps.com/l/cbCaiohag6ao06mA","pdf",571681,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# 1. INTRODUCTION","[{\"question\":\"What is the study’s main goal regarding osteoporosis risk factors?\",\"answer\":\"The study compares prediction accuracy between five machine learning methods and traditional multiple linear regression for T-score of bone mineral density, and ranks the importance of 25 risk factors.\"},{\"question\":\"How was the study sample defined and what models were used?\",\"answer\":\"The sample included 24,412 women older than 55 years and used traditional MLR plus five machine learning methods: classification and regression tree, Naïve Bayes, random forest, stochastic gradient boosting, and eXtreme gradient boosting.\"},{\"question\":\"Which factors were most important for predicting T-score?\",\"answer\":\"Across machine learning methods, age was the most important factor, followed by eGFR, body mass index (BMI), uric acid (UA), and education level.\"}]","Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort | PDF",1790677165,23]