Applying machine learning techniques to the identification of late-onset hypogonadism in elderly men

NCUIR > School of Management at National Central University > Department of Information Management > journal & Dissertation > Item 987654321/106417

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106417

Title:	Applying machine learning techniques to the identification of late-onset hypogonadism in elderly men
Authors:	蔡志豐;Lu, Ti;Hu, Ya-Han;Tsai, Chih-Fong;Liu, Shih-Ping;Chen, Pei-Ling
Contributors:	管理學院資訊管理學系
Keywords:	Aging;Humanities and Social Sciences;Medicine;Metabolic disorders;multidisciplinary;Older people;Prediction models;Science;Science (multidisciplinary)
Date:	2016-12-01
Issue Date:	2026-04-23 13:22:03 (UTC+8)
Publisher:	Springer Science and Business Media Deutschland GmbH;Cham: Springer Science and Business Media LLC
Abstract:	摘要： In the diagnosis of late-onset hypogonadism (LOH), the Androgen Deficiency in the Aging Male (ADAM) questionnaire or Aging Males’ Symptoms (AMS) scale can be used to assess related symptoms. Subsequently, blood tests are used to measure serum testosterone levels. However, results obtained using ADAM and AMS have revealed no significant correlations between ADAM and AMS scores and LOH, and the rate of misclassification is high. Recently, many studies have reported significant associations between clinical conditions such as the metabolic syndrome, obesity, lower urinary tract symptoms, and LOH. In this study, we sampled 772 clinical cases of men who completed both a health checkup and two questionnaires (ADAM and AMS). The data were obtained from the largest medical center in Taiwan. Two well-known classification techniques, the decision tree (DT) and logistic regression, were used to construct LOH prediction models on the basis of the aforementioned features. The results indicate that although the sensitivity of ADAM is the highest (0.878), it has the lowest specificity (0.099), which implies that ADAM overestimates LOH occurrence. In addition, DT combined with the AdaBoost technique (AdaBoost DT) has the second highest sensitivity (0.861) and specificity (0.842), resulting in having the best accuracy (0.851) among all classifiers. AdaBoost DT can provide robust predictions that will aid clinical decisions and can help medical staff in accurately assessing the possibilities of LOH occurrence. 其他題名： SpringerPlus 其他題名： Springerplus 出版者： Cham: Springer Science and Business Media LLC 出版日期： 2016-06-16 出處： SpringerPlus, 2016-06, Vol.5 (1), p.729-, Article 729 資源來源： Agricultural & Environmental Science Collection 版權： The Author(s) 2016 版權： SpringerPlus is a copyright of Springer, 2016. 識別號： ISSN: 2193-1801 識別號： EISSN: 2193-1801 識別號： DOI: 10.1186/s40064-016-2531-8 識別號： PMID: 27375998
Appears in Collections:	[Department of Information Management] journal & Dissertation

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