[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122767-en":3,"doc-seo-122767-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122767,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning-based prediction of osteoporosis in postmenopausal women with clinical examined features - A quantitative clinical study","Osteoporosis is a silent skeletal disorder common in older adults and often overlooked, so earlier identification is crucial for prevention of fractures and disability. This study proposes an advanced machine learning prediction framework to detect the potential occurrence of osteoporosis using clinical screening features. Multiple machine learning methods are trained and compared on two datasets: Taiwan clinical tests and Korean postmenopausal medical records from 2010–2011. Performance is evaluated with AUC and other metrics, and results are found to be more reliable than the Osteoporosis Self-Assessment Tool for Asians. Early risk detection supports timely clinical action to reduce adverse outcomes.","Received: 13 July 2023  \nRevised: 30 September 2023  \nAccepted: 11 October 2023  \nDOI: 10.1002/hsr2.1656  \nORIG INA L RESEARCH    \nMachine learning‐based prediction of osteoporosis in postmenopausal women with clinical examined features:  \nA quantitative clinical study  \nKainat A. Ullah1 | Faisal Rehman1,2 | Muhammad Anwar3  | Muhammad Faheem4  | Naveed Riaz5  \n1Department of Computer Science and Information Technology, Lahore Leads University, Lahore, Pakistan  \n2Department of Statistics and Data Science, University of Mianwali, Mianwali, Pakistan 3Department of Information  \nSciences, Division of Science and Technology, University of Education, Lahore, Pakistan 4School of Technology and Innovations, University of Vaasa, Vaasa, Finland  \n5School of Electrical Engineering and Computer Science (SEECS), National University of Sciences & Technology, Islamabad, Pakistan  \nCorrespondence  \nMuhammad Faheem [Email: muhammad.faheem@uwasa.fi](Email: muhammad.faheem@uwasa.fi)  \nFunding information None  \nAbstract  \nOsteoporosis is a skeletal disease that is commonly seen in older people but often neglected due to its silent nature. To overcome the issue of osteoporosis in men and women, we proposed an advanced prediction model with the help of machine learning techniques which can help to identify the potential occurrence of this bone disease by its advanced screening tools. To achieve more reliable and accurate results, various machine‐learning techniques were applied to the presented datasets. Moreover, we also compared the performance of our results with other existing algorithms to solely focus on the advanced features of the proposed methodology. The two data sets, the clinical tests of patients in Taiwan and medical reports of postmenopausal women in Korea through Korean Health and Nutrition Examination Surveys (2010–2011) were considered in this study. To predict bone disorders, we utilized the data about females and developed a system using artificial neural networks, support vector machines, and K‐nearest neighbor. To compare the performance of the model Area under the Receiver Operating Characteristic Curve and other evaluation metrics were compared. The achieved results from all the algorithms and compared them with Osteoporosis Self‐Assessment Tool for Asians and the results were noticeably better and more reliable than existing systems due to the involvement of ML. Using machine learning techniques to predict these types of diseases is better because physicians and patients can take early action to prevent the consequences in advance.  \nKEYWO R DS  \nclassification, machine learning, osteoporosis, osteoporotic fractures, prediction  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2023 The Authors. Health Science Reports published by Wiley Periodicals LLC.  \nHealth Sci. Rep. 2023;6:e1656 . [https://doi.org/10.1002/hsr2.1656](https://doi.org/10.1002/hsr2.1656)  \n[wileyonlinelibrary.com/journal/hsr2](wileyonlinelibrary.com/journal/hsr2)  \n1 of 10  \n2 of 10  \nULLAH ET AL.  \n1 | INTRODUCTION  \nIn 1993, osteoporosis was discovered as a skeletal disease that is caused by low bone density,1,2 dislocation of bone structure, and also increases the fragility and sensitivity of bones. Osteoporosis is often neglected or slightly considered around the globe but this disease has a great negative impact on the health of humans because it can cause disability and in severe cases, it can also cause mortality.3 Mostly in older people, this bone disease is increasing rapidly4 but there are no necessary preventive measures are taken which is causing higher rates of bone fractures and other vertebral diseases. The expansion rate of osteoporosis4 and causing fractures is increasing in more than 50‐year‐ old individuals. Proper medication, precautionary measures, and maintenance of proper lif","cbCaivyCCjlTogVI","https://ap.wps.com/l/cbCaivyCCjlTogVI","pdf",761321,1,10,"English","en",105,"# Introduction\n## Disease background and clinical importance\n## Risk factors and need for screening\n# Methods\n## Datasets and study design\n## Prediction models\n## Evaluation metrics","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses the early prediction of osteoporosis in postmenopausal women to enable timely prevention despite the disease’s silent nature.\"},{\"question\":\"Which data sources are used to train and test the prediction models?\",\"answer\":\"It uses clinical tests from Taiwan and medical reports of postmenopausal women in Korea from the Korean Health and Nutrition Examination Surveys (2010–2011).\"},{\"question\":\"How are the prediction models evaluated in the study?\",\"answer\":\"Model performance is compared using the Area under the Receiver Operating Characteristic Curve (AUC) and other evaluation metrics, then benchmarked against the Osteoporosis Self-Assessment Tool for Asians.\"}]","Machine learning-based prediction of osteoporosis in postmenopausal women with clinical examined features - A quantitative clinical study | PDF",1785812798,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-prediction-of-osteoporosis-in-postmenopausal-women-with-clinical-examined-features-a-quantitative-clinical-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-prediction-of-osteoporosis-in-postmenopausal-women-with-clinical-examined-features-a-quantitative-clinical-study/122767/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the document address?","Question",{"text":75,"@type":76},"It addresses the early prediction of osteoporosis in postmenopausal women to enable timely prevention despite the disease’s silent nature.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data sources are used to train and test the prediction models?",{"text":80,"@type":76},"It uses clinical tests from Taiwan and medical reports of postmenopausal women in Korea from the Korean Health and Nutrition Examination Surveys (2010–2011).",{"name":82,"@type":73,"acceptedAnswer":83},"How are the prediction models evaluated in the study?",{"text":84,"@type":76},"Model performance is compared using the Area under the Receiver Operating Characteristic Curve (AUC) and other evaluation metrics, then benchmarked against the Osteoporosis Self-Assessment Tool for Asians.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]