{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T13:16:59Z","timestamp":1782825419459,"version":"3.54.5"},"reference-count":51,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Thailand Science Research and Innovation (TSRI) National Science, Research and Innovation Fund (NSRF)","award":["REC 64.1129-189-7534"],"award-info":[{"award-number":["REC 64.1129-189-7534"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Osteoporosis is still a serious public health issue in Thailand, particularly in postmenopausal women; meanwhile, new effective screening tools are required for rapid diagnosis. This study constructs and confirms an osteoporosis screening tool-based decision tree (DT) model. Four DT algorithms, namely, classification and regression tree; chi-squared automatic interaction detection (CHAID); quick, unbiased, efficient statistical tree; and C4.5, were implemented on 356 patients, of whom 266 were abnormal and 90 normal. The investigation revealed that the DT algorithms have insignificantly different performances regarding the accuracy, sensitivity, specificity, and area under the curve. Each algorithm possesses its characteristic performance. The optimal model is selected according to the performance of blind data testing and compared with traditional screening tools: Osteoporosis Self-Assessment for Asians and the Khon Kaen Osteoporosis Study. The Decision Tree for Postmenopausal Osteoporosis Screening (DTPOS) tool was developed from the best performance of CHAID\u2019s algorithms. The age of 58 years and weight at a cutoff of 57.8 kg were the essential predictors of our tool. DTPOS provides a sensitivity of 92.3% and a positive predictive value of 82.8%, which might be used to rule in subjects at risk of osteopenia and osteoporosis in a community-based screening as it is simple to conduct.<\/jats:p>","DOI":"10.3390\/informatics9040083","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T02:54:25Z","timestamp":1666148065000},"page":"83","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Decision Tree Modeling for Osteoporosis Screening in Postmenopausal Thai Women"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1718-3021","authenticated-orcid":false,"given":"Bunjira","family":"Makond","sequence":"first","affiliation":[{"name":"Faculty of Commerce and Management, Prince of Songkla University, Trang 92000, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7254-2448","authenticated-orcid":false,"given":"Pornsarp","family":"Pornsawad","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, Silpakorn University, Nakorn Pathom 73000, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8450-1619","authenticated-orcid":false,"given":"Kittisak","family":"Thawnashom","sequence":"additional","affiliation":[{"name":"Department of Medical Technology, Faculty of Allied Health Sciences, Naresuan University, Phitsanulok 65000, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,19]]},"reference":[{"key":"ref_1","unstructured":"WHO (2022, September 07). Prevention and Management of Osteoporosis, Available online: https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/15293701."},{"key":"ref_2","unstructured":"National Statistical Office (2008). Survey Report of the Elderly Population in Thailand 2007."},{"key":"ref_3","unstructured":"Gu, D., and Dupre, M.E. (2019). National Survey of Older Persons in Thailand. Encyclopedia of Gerontology and Population Aging, Springer International Publishing."},{"key":"ref_4","first-page":"910","article-title":"Development and validation of a new clinical risk index for prediction of osteoporosis in Thai women","volume":"87","author":"Pongchaiyakul","year":"2004","journal-title":"J. Med. Assoc. Thail."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1111\/j.1447-0756.2004.00224.x","article-title":"Diagnostic performance of quantitative ultrasound calcaneus measurement in case finding for osteoporosis in Thai postmenopausal women","volume":"30","author":"Panichkul","year":"2004","journal-title":"J. Obstet. Gynaecol. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s001980170070","article-title":"A simple tool to identify Asian women at increased risk of osteoporosis","volume":"12","author":"Koh","year":"2001","journal-title":"Osteoporos. Int."},{"key":"ref_7","first-page":"9","article-title":"Validation of the KKOS scoring system for Screening of Osteoporosis in Thai Elderly Woman aged 60 years and older","volume":"24","author":"Prommahachai","year":"2009","journal-title":"Srinagarind Med. J."},{"key":"ref_8","first-page":"86","article-title":"A study on osteoporosis screening tool for Chinese women","volume":"21","author":"Zhang","year":"2007","journal-title":"Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi = Zhongguo Xiufu Chongjian Waike Zazhi Chin. J. Reparative Reconstr. Surg."},{"key":"ref_9","unstructured":"Kanis, J.A. (2022, August 16). Fracture Risk Assessment Tool. Centre for Metabolic Bone Disease, University of Sheffield. Available online: https:\/\/www.sheffield.ac.uk\/FRAX\/tool.aspx?lang=en."},{"key":"ref_10","first-page":"111","article-title":"Validation of FRAX\u00ae WHO Fracture Risk Assessment Tool with and without the Alara Metriscan Phalangeal Densitometer as a screening tool to identify osteoporosis in Thai postmenopausal women","volume":"20","author":"Yingyuenyong","year":"2012","journal-title":"Thai J. Obstet. Gynaecol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1067\/mob.2000.106594","article-title":"Identification of at-risk women for osteoporosis screening","volume":"183","author":"Weinstein","year":"2000","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_12","first-page":"1289","article-title":"Development and validation of the Osteoporosis Risk Assessment Instrument to facilitate selection of women for bone densitometry","volume":"162","author":"Cadarette","year":"2000","journal-title":"CMAJ"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1080\/gye.16.3.245.250","article-title":"Development and assessment of the Osteoporosis Index of Risk (OSIRIS) to facilitate selection of women for bone densitometry","volume":"16","author":"Sedrine","year":"2002","journal-title":"Gynecol. Endocrinol."},{"key":"ref_14","first-page":"37","article-title":"Development and validation of a simple questionnaire to facilitate identification of women likely to have low bone density","volume":"4","author":"Lydick","year":"1998","journal-title":"Am. J. Manag. Care"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1007\/s001980170072","article-title":"An assessment tool for predicting fracture risk in postmenopausal women","volume":"12","author":"Black","year":"2001","journal-title":"Osteoporos. Int."},{"key":"ref_16","first-page":"69","article-title":"Osteoporosis Risk Prediction Using Data Mining Algorithms","volume":"9","author":"Jabarpour","year":"2020","journal-title":"J. Community Health Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.bone.2018.04.020","article-title":"Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report","volume":"116","author":"Kim","year":"2018","journal-title":"Bone"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e10337","DOI":"10.1002\/jbm4.10337","article-title":"A Novel Fracture Prediction Model Using Machine Learning in a Community-Based Cohort","volume":"4","author":"Kong","year":"2020","journal-title":"JBMR Plus"},{"key":"ref_19","first-page":"28","article-title":"Intelligent predictive osteoporosis system","volume":"32","author":"Moudani","year":"2011","journal-title":"Int. J. Comput. Appl."},{"key":"ref_20","first-page":"886","article-title":"Wang, W.; Richards, G.; Rea, S. Hybrid data mining ensemble for predicting osteoporosis risk","volume":"2006","author":"Wang","year":"2005","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.3349\/ymj.2013.54.6.1321","article-title":"Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning","volume":"54","author":"Yoo","year":"2013","journal-title":"Yonsei Med. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.cmpb.2017.02.001","article-title":"hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm","volume":"141","author":"Tayefi","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.ijinfomgt.2017.10.002","article-title":"Predicting service industry performance using decision tree analysis","volume":"38","author":"Yeo","year":"2018","journal-title":"Int. J. Inf. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1061\/(ASCE)0887-3801(2005)19:4(387)","article-title":"Predicting the outcome of construction litigation using boosted decision trees","volume":"19","author":"Arditi","year":"2005","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1061\/(ASCE)0887-3801(2004)18:2(132)","article-title":"Decision tree approach to classify and quantify cumulative impact of change orders on productivity","volume":"18","author":"Lee","year":"2004","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, L., Si, M., Ma, H., Cong, M., Xu, Q., Sun, Q., Wu, W., Wang, C., Fagan, M.J., and Mur, L. (2022). A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images. BMC Bioinform., 23.","DOI":"10.1186\/s12859-022-04596-z"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.cmpb.2017.04.011","article-title":"Application of data mining techniques and data analysis methods to measure cancer morbidity and mortality data in a regional cancer registry: The case of the island of Crete, Greece","volume":"145","author":"Varlamis","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e013336","DOI":"10.1136\/bmjopen-2016-013336","article-title":"Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: A decade follow-up in a Middle East prospective cohort study","volume":"6","author":"Ramezankhani","year":"2016","journal-title":"BMJ Open"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103991","DOI":"10.1016\/j.compbiomed.2020.103991","article-title":"Clinical data classification using an enhanced SMOTE and chaotic evolutionary feature selection","volume":"126","author":"Sreejith","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1016\/j.eswa.2007.12.002","article-title":"Using Kaplan\u2013Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4. 5 and ID3) in determining recurrence-free survival of breast cancer patients","volume":"36","author":"Ture","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_31","unstructured":"Han, J., Pei, J., and Kamber, M. (2011). Data Mining: Concepts and Techniques, Elsevier."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rokach, L., and Maimon, O. (2005). Decision trees. Data Mining and Knowledge Discovery Handbook, Springer.","DOI":"10.1007\/0-387-25465-X_9"},{"key":"ref_33","first-page":"178","article-title":"Significance testing in automatic interaction detection (AID)","volume":"24","author":"Kass","year":"1975","journal-title":"J. R. Stat. Soc. Ser. C"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.artmed.2010.05.002","article-title":"Missing data imputation using statistical and machine learning methods in a real breast cancer problem","volume":"50","author":"Jerez","year":"2010","journal-title":"Artif. Intell. Med."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rajula, H.S.R., Verlato, G., Manchia, M., Antonucci, N., and Fanos, V. (2020). Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment. Medicina, 56.","DOI":"10.3390\/medicina56090455"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1097\/00042192-200101000-00011","article-title":"Prevalence of osteopenia and osteoporosis in Thai women","volume":"8","author":"Limpaphayom","year":"2001","journal-title":"Menopause"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.afos.2016.10.002","article-title":"Thai Osteoporosis Foundation (TOPF) position statements on management of osteoporosis","volume":"2","author":"Songpatanasilp","year":"2016","journal-title":"Osteoporos. Sarcopenia"},{"key":"ref_38","first-page":"59","article-title":"Factors related to mortality after osteoporotic hip fracture treatment at Chiang Mai University Hospital, Thailand, during 2006 and 2007","volume":"98","author":"Chaysri","year":"2015","journal-title":"J. Med. Assoc. Thai"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.afos.2015.09.003","article-title":"Validation of the thai osteoporosis foundation and royal college of orthopaedic surgeons of Thailand clinical practice guideline for bone mineral density measurement in postmenopausal women","volume":"1","author":"Suwan","year":"2015","journal-title":"Osteoporos. Sarcopenia"},{"key":"ref_40","unstructured":"Clague, C. (2022, August 16). Thailand: Osteoporosis Moves Up the Health Policy Agenda. The Economist Intelligence Unit Limited 2021. Available online: https:\/\/impact.economist.com\/perspectives\/perspectives\/sites\/default\/files\/eco114_amgen_thailand_and_philippines_1_3.pdf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1080\/13697137.2016.1231176","article-title":"Validation of osteoporosis risk assessment tools in middle-aged Thai women","volume":"19","author":"Indhavivadhana","year":"2016","journal-title":"Climacteric"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Mithal, A., Ebeling, P., and Kyer, C.S. (2013). The Asia-Pacific Regional Audit: Epidemiology, Costs&burden of Osteoporosis in 2013, International Osteoporosis Foundation.","DOI":"10.4103\/2230-8210.137485"},{"key":"ref_43","first-page":"261","article-title":"Burden of osteoporosis in Thailand","volume":"91","author":"Pongchaiyakul","year":"2008","journal-title":"J. Med. Assoc. Thail."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1007\/s00198-015-3025-1","article-title":"Systematic review and meta-analysis of the performance of clinical risk assessment instruments for screening for osteoporosis or low bone density","volume":"26","author":"Nayak","year":"2015","journal-title":"Osteoporos. Int."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e3415","DOI":"10.1097\/MD.0000000000003415","article-title":"Comparisons of different screening tools for identifying fracture\/osteoporosis risk among community-dwelling older people","volume":"95","author":"Chen","year":"2016","journal-title":"Medicine"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.afos.2019.09.001","article-title":"A comparison of 6 osteoporosis risk assessment tools among postmenopausal women in Kuala Lumpur, Malaysia","volume":"5","author":"Toh","year":"2019","journal-title":"Osteoporos. Sarcopenia"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Bui, M.H., Dao, P.T., Khuong, Q.L., Le, P.-A., Nguyen, T.-T.T., Hoang, G.D., Le, T.H., Pham, H.T., Hoang, H.-X.T., and Le, Q.C. (2022). Evaluation of community-based screening tools for the early screening of osteoporosis in postmenopausal Vietnamese women. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0266452"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.nut.2009.12.001","article-title":"Vitamin D status and bone health in healthy Thai elderly women","volume":"27","author":"Chailurkit","year":"2011","journal-title":"Nutrition"},{"key":"ref_49","first-page":"e26518","article-title":"Osteoporosis Screening and Fracture Risk Assessment Tool: Its Scope and Role in General Clinical Practice","volume":"14","author":"Chavda","year":"2022","journal-title":"Cureus"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/5584_2019_413","article-title":"Genetic Predisposition for Osteoporosis and Fractures in Postmenopausal Women","volume":"1211","author":"Mitek","year":"2019","journal-title":"Adv. Exp. Med. Biol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s11739-018-1874-2","article-title":"Guidelines for the management of osteoporosis and fragility fractures","volume":"14","author":"Nuti","year":"2019","journal-title":"Intern. Emerg. Med."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/9\/4\/83\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:56:56Z","timestamp":1760144216000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/9\/4\/83"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,19]]},"references-count":51,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["informatics9040083"],"URL":"https:\/\/doi.org\/10.3390\/informatics9040083","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,19]]}}}