[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125513-en":3,"doc-seo-125513-105":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":4,"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},125513,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Predicting Medication Wastage Using Machine Learning Based on Patient Beliefs","Medication wastage threatens the sustainability of subsidised healthcare systems in Southeast Asia because financial and resource constraints magnify losses from unused, expired, or contaminated medicines. This study develops a machine learning model to predict medication wastage by linking patient demographics, health conditions, and beliefs about medicines, using Malaysia as the case study. A cross-sectional survey of 734 patients from six public facilities was analyzed with multiple ML regression models evaluated via RMSE.","Original research article  \nPredicting medication wastage using machine learning based on patient beliefs  \nDIGITAL HEALTH Volume 11: 1–18 © The Author(s) 2025 Article reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/20552076251355127](DOI: 10.1177/20552076251355127)[ ](DOI: 10.1177/20552076251355127)[journals.sagepub.com/home/dhj](journals.sagepub.com/home/dhj)  \nFirdaus Aziz 1, Sorayya Malek2 , Shubathira Sooriamoorthy3,4,  \nIlham Asyilah Mahamood2, Chong Wei Wen3, Sharifah M. Syed Ahmad5, Putri Nur Fatin Amir Rudin2 and Adliah Mhd Ali3  \nAbstract  \nObjectives: Medication wastage is a critical issue impacting the sustainability of subsidised healthcare systems in Southeast Asia due to financial and resource constraints. This study aimed to develop a machine learning (ML) model to predict medication wastage by analysing patient demographics, health conditions and beliefs about medicines, using Malaysia as a case study. Methods: A cross-sectional survey was conducted involving 734 patients across six public healthcare facilities in Malaysia. Data on demographics, medication history and beliefs about medicines were collected using validated questionnaires. Multiple ML regression models were evaluated to predict medication wastage, with performance assessed based on root mean squared error (RMSE).  \nResults: The XGBoost model achieved the best performance with the lowest RMSE of 4.67, outperforming other models (RMSE range:4.68–5.10). It also performed best using only seven features selected by sequential backward elimination method using LR, making it practical for clinical implementation. Key predictors of medication wastage included beliefs about medicines, age, ethnicity, region and monthly income.  \nConclusion: This study is the first to apply ML to address medication wastage in a Southeast Asian context, filling a critical research gap. The proposed model provides a foundation for developing targeted interventions to reduce medication wastage and supports policymakers and healthcare providers in optimising the allocation of subsidised medications. The insights are broadly applicable to other countries with similar healthcare resource challenges.  \nKeywords  \nMedication wastage, machine learning, predictive modelling, Southeast Asia, healthcare sustainability, Malaysia  \nReceived: 22 November 2024; accepted: 11 June 2025  \n1 Pusat Pengajian Citra Universiti, Universiti Kebangsaan Malaysia, Bandar Baru Bangi, Selangor, Malaysia  \n2Bioinformatics Science Programme, Institute of Biological Sciences, Universiti Malaya, Kuala Lumpur, Malaysia  \n3Center for Quality Management of Medicines (QMM), Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia 4Pharmacy Department, Tengku Ampuan Rahimah Hospital, Klang, Selangor, Malaysia  \n5Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia  \nFirdaus Aziz, Sorayya Malek, Ilham Asyilah Mahamood, and Adliah Mhd Ali contributed equally to this work.  \nCorresponding authors:  \nSorayya Malek, Bioinformatics Science Programme, Institute of Biological Sciences, Universiti Malaya, Kuala Lumpur, Malaysia.  \nEmail: [sorayya@um.edu.my](sorayya@um.edu.my)  \nAdliah Mhd Ali, Center for Quality Management of Medicines (QMM), Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.  \nEmail: [adliah@ukm.edu.my](adliah@ukm.edu.my)  \nCreative Commons NonCommercial-NoDerivs CC BY-NC-ND: This article is distributed under the terms of the Creative Commons  \nAttribution-NonCommercial-NoDerivs 4.0 License ([https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)) which permits noncommercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access page ([https://us.sagepub.com/en-u","cbCaieTRcqwD95tV","https://ap.wps.com/l/cbCaieTRcqwD95tV","pdf",912236,1,18,"English","en",105,"# Abstract\n## Objectives\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What problem does the study target in Southeast Asia?\",\"answer\":\"The study targets medication wastage in subsidised healthcare systems, which undermines sustainability due to financial and resource constraints.\"},{\"question\":\"How was the dataset collected for the machine learning model?\",\"answer\":\"A cross-sectional survey collected data from 734 patients across six public healthcare facilities in Malaysia using validated questionnaires.\"},{\"question\":\"Which machine learning model performed best and what key predictors were identified?\",\"answer\":\"XGBoost achieved the lowest RMSE of 4.67. 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