[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121007-en":3,"doc-seo-121007-105":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},121007,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Predicting Hospital Readmissions in Diabetes Patients - A Comparative Study of Machine Learning Models","Diabetes is a chronic condition with high hospital readmission rates within 30 days, creating substantial healthcare costs and resource strain. This study builds and compares six machine learning classifiers—Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CATBoost—to predict readmission risk for diabetes patients using the Diabetes 130-US hospitals dataset from UCI. Models are evaluated with accuracy, precision, recall, and AUC-ROC, and CATBoost achieves the strongest performance (AUC 0.70; accuracy 64.2%). Findings highlight inpatient visit counts, medication usage, and length of stay as key predictors, supporting targeted interventions and future real-time data integration.","International Journal of Health Sciences  \n[Available online at www.sciencescholar.us](Available online at www.sciencescholar.us)[ ](Available online at www.sciencescholar.us)[Vol. 8 No. 3](Vol. 8 No. 3), December 2024, pages: 289-297  \nE-ISSN: 2550-696X  \n[https://doi.org/10.53730/ijhs.v8n3.15189](https://doi.org/10.53730/ijhs.v8n3.15189)  \nPredicting Hospital Readmissions in Diabetes Patients: A Comparative Study of Machine Learning Models  \nAlekhya Gandra a  \nManuscript submitted: 09 May 2024, Manuscript revised: 18 August 2024, Accepted for publication: 24 September 2024  \nCorresponding Author a Abstract  \nKeywords  \ndiabetes;  \nhealthcare analytics; hospital readmission; machine learning; predictive modelling;  \nObjective: Diabetes is a chronic condition affecting millions worldwide, requiring continuous medical care to manage and prevent complications (Powers & D'Alessio, 2016) . Hospital readmission rates among diabetes patients are high, contributing to significant healthcare costs and resourcestrain (Zeng & Liu, 2015). This study aims to predict hospital readmissions for diabetes patients using machine learning (ML) techniques to identify highrisk patients and reduce unnecessary readmissions. Methodology: Six machine learning algorithms—Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CATBoost—were used to classify patients based on their likelihood of being readmitted within 30 days. The Diabetes 130-US hospitals dataset from the UCI Machine Learning Repository (Stracket al., 2014) was employed, leveraging demographic, clinical, and dischargerelated variables to build predictive models. Performance metrics such as accuracy, precision, recall, and AUC-ROC were used to evaluate the models. Results: The CATBoost classifier performed the best, achieving an AUC score of 0.70 and an accuracy of 64.2% . Key predictive features included the number of inpatient visits, medications, and the duration of hospital stays. The results emphasize the value of machine learning in predicting hospital readmissions, offering actionable insights for healthcare providers. Conclusion: This study demonstrates the effectiveness of machine learning, particularly CATBoost, in identifying high-risk diabetes patients for targeted interventions. These models can improve patient outcomes and optimize healthcare resources by reducing unnecessary readmissions. Future research should explore integrating real-time health data from wearables and the influence of social determinants on readmission rates to enhance predictive accuracy.  \nInternational Journal of Health Sciences © 2024.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)).  \na Atlanta, Georgia, United States  \nContents  \nAbstract ............................................................................................................................................................................................................ 289  \n1 Introduction ............................................................................................................................................................................................ 290  \n2 Materials and Methods ...................................................................................................................................................................... 290  \n3 Results and Discussions .................................................................................................................................................................... 291  \n3.1 Results ............................................................................................................................................................................................... 291  \n3.2 Discussions .................................................................................................","cbCait80SdNev1zT","https://ap.wps.com/l/cbCait80SdNev1zT","pdf",666031,1,9,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 Materials and Methods\n# 3 Results and Discussions\n## 3.1 Results\n## 3.2 Discussions\n# 4 Conclusion\n# Acknowledgments\n# References\n# Biography of Authors","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To predict hospital readmissions for diabetes patients using machine learning models so that high-risk patients can be identified and unnecessary readmissions reduced.\"},{\"question\":\"Which dataset and time window are used for prediction?\",\"answer\":\"The study uses the Diabetes 130-US hospitals dataset from the UCI Machine Learning Repository, and models predict whether patients will be readmitted within 30 days.\"},{\"question\":\"Which machine learning model performed best and how was performance measured?\",\"answer\":\"CATBoost performed best, evaluated using accuracy, precision, recall, and AUC-ROC, with an AUC of 0.70 and accuracy of 64.2%.\"}]","Predicting Hospital Readmissions in Diabetes Patients - A Comparative Study of Machine Learning Models | PDF",1785733280,23,{"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},"predicting-hospital-readmissions-in-diabetes-patients-a-comparative-study-of-machine-learning-models","",{"@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/predicting-hospital-readmissions-in-diabetes-patients-a-comparative-study-of-machine-learning-models/121007/",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","2026-08-03",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 is the main objective of the study?","Question",{"text":75,"@type":76},"To predict hospital readmissions for diabetes patients using machine learning models so that high-risk patients can be identified and unnecessary readmissions reduced.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and time window are used for prediction?",{"text":80,"@type":76},"The study uses the Diabetes 130-US hospitals dataset from the UCI Machine Learning Repository, and models predict whether patients will be readmitted within 30 days.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and how was performance measured?",{"text":84,"@type":76},"CATBoost performed best, evaluated using accuracy, precision, recall, and AUC-ROC, with an AUC of 0.70 and accuracy of 64.2%.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]