[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121911-en":3,"doc-seo-121911-105":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},121911,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery - Comprehensive study","Metabolic bariatric surgery is a pivotal intervention for obesity-related health issues, yet patient outcomes vary and require accurate classification and prediction to support optimal treatment strategies. This study proposes a machine learning approach to classify postoperative success in a cohort of 73 patients, comparing multiple models and input variable types. Results evaluate predictive performance and assess the effect of variable groupings and oversampling methods to improve model efficacy.","Original Research Article  \nApplication of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study  \nJosé Alberto Benítez-Andrades1 , Camino Prada-García2,3  , Rubén García-Fernández4, María D Ballesteros-Pomar5  ,  \nMaría-Inmaculada González-Alonso4 and Antonio Serrano-García6   \nAbstract  \nDIGITAL HEALTH Volume 10: 1–10  \n© The Author(s) 2024  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/20552076241239274](DOI: 10.1177/20552076241239274)[ ](DOI: 10.1177/20552076241239274)[journals.sagepub.com/home/dhj](journals.sagepub.com/home/dhj)  \nObjectives: Metabolic bariatric surgery is a critical intervention for patients living with obesity and related health issues. Accurate classification and prediction of patient outcomes are vital for optimizing treatment strategies. This study presentsa novel machine learning approach to classify patients in the context of metabolic bariatric surgery, providing insights into the efficacy of different models and variable types.  \nMethods: Various machine learning models, including Gaussian Naive Bayes, Complement Naive Bayes, K-nearest neighbour, Decision Tree, K-nearest neighbour with RandomOverSampler, and K-nearest neighbour with SMOTE, were applied to a dataset of 73 patients. The dataset, comprising psychometric, socioeconomic, and analytical variables, was analyzed to determine the most efficient predictive model. The study also explored the impact of different variable groupings and oversampling techniques.  \nResults: Experimental results indicate average accuracy values as high as 66.7% for the best model. Enhanced versions of Knearest neighbour and Decision Tree, along with variations of K-nearest neighbour such as RandomOverSampler and SMOTE, yielded the best results.  \nConclusions: The study unveils a promising avenue for classifying patients in the realm of metabolic bariatric surgery. The results underscore the importance of selecting appropriate variables and employing diverse approaches to achieve optimal performance. The developed system holds potential as a tool to assist healthcare professionals in decision-making, thereby enhancing metabolic bariatric surgery outcomes. These findings lay the groundwork for future collaboration between hospitals and healthcare entities to improve patient care through the utilization of machine learning algorithms. Moreover, the findings suggest room for improvement, potentially achievable with a larger dataset and careful parameter tuning.  \nKeywords  \nMetabolic bariatric surgery, machine learning, predictive model, oversampling techniques, patient outcomes  \nSubmission date: 14 August 2023; Acceptance date: 13 February 2024  \n\n| 1SALBIS Research Group, Department of Electric, Systems and Automatics Engineering, Universidad de León, León, Spain\u003Cbr>2Department of Preventive Medicine and Public Health, University of Valladolid, Valladolid, Spain\u003Cbr>3Dermatology Service, Complejo Asistencial Universitario de León, León, Spain 4Department of Electric, Systems and Automatics Engineering, Escuela de Ingenierías Industrial, Informática y Aeroespacial, Universidad de León, León, Spain | 5Department of Endocrinology and Nutrition, Complejo Asistencial Universitario de León, León, Spain\u003Cbr>6Psychiatry Service, Department of Psychosomatic, Complejo Asistencial Universitario de León, León, Spain\u003Cbr>Corresponding author:\u003Cbr>Camino Prada-García, Department of Preventive Medicine and Public Health, University of Valladolid, 47005 Valladolid, Spain.\u003Cbr>Email:cprada@saludcasti[llayleon.es](llayleon.es) |\n| --- | --- |\n\nCreative Commons NonCommercial-NoDerivs CC BY-NC-ND: This article is distributed under the terms of the Creative Commons Attribution  \nNoDerivs 4.0 License ([https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)) which permits any use, reprod","cbCaiaAQOZH8Ok5E","https://ap.wps.com/l/cbCaiaAQOZH8Ok5E","pdf",1373225,1,10,"English","en",105,"# Abstract\n## Objectives\n## Methods\n## Results\n## Conclusions\n# Introduction and related work\n## Variability in outcomes after metabolic bariatric surgery\n## Predictors of postoperative outcomes\n## Role of preoperative vs early postoperative weight loss","[{\"question\":\"What is the main objective of the study on metabolic bariatric surgery outcomes?\",\"answer\":\"To build and evaluate a machine learning approach that classifies postoperative success and compares model effectiveness across different variable types.\"},{\"question\":\"Which machine learning models were tested in the study?\",\"answer\":\"The study applied Gaussian Naive Bayes, Complement Naive Bayes, K-nearest neighbour, Decision Tree, and K-nearest neighbour variants using RandomOverSampler and SMOTE.\"},{\"question\":\"How did the study attempt to improve predictive performance?\",\"answer\":\"It explored different groupings of variables and used oversampling techniques (RandomOverSampler and SMOTE) to enhance results.\"}]","Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery - Comprehensive study | PDF",1785807702,25,{"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},"application-of-machine-learning-algorithms-in-classifying-postoperative-success-in-metabolic-bariatric-surgery-comprehensive-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/application-of-machine-learning-algorithms-in-classifying-postoperative-success-in-metabolic-bariatric-surgery-comprehensive-study/121911/",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":4},"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 on metabolic bariatric surgery outcomes?","Question",{"text":75,"@type":76},"To build and evaluate a machine learning approach that classifies postoperative success and compares model effectiveness across different variable types.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models were tested in the study?",{"text":80,"@type":76},"The study applied Gaussian Naive Bayes, Complement Naive Bayes, K-nearest neighbour, Decision Tree, and K-nearest neighbour variants using RandomOverSampler and SMOTE.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the study attempt to improve predictive performance?",{"text":84,"@type":76},"It explored different groupings of variables and used oversampling techniques (RandomOverSampler and SMOTE) to enhance results.","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"]