[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119455-en":3,"doc-seo-119455-105":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},119455,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Exploring Prediabetes Pathways - Using Machine Learning and Counterfactual Explanations for Type 2 Diabetes Prediction and Prevention","Type 2 Diabetes Mellitus (T2DM) remains a major global health challenge, while prediabetes is an intermediate, reversible state that enables timely prevention. This study uses an ad hoc dataset from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) to train machine learning models predicting T2DM onset as well as progression to prediabetes and maintenance of normoglycemia. Post hoc explainability methods (feature importance, partial dependence, and instance-level waterfall plots) clarify model reasoning, highlighting FBS and HbA1c, followed by BMI, HDL, and LDL, with smaller effects from blood pressure. Counterfactual explanations support clinicians in devising personalized interventions to prevent progression.","Exploring Prediabetes Pathways: Using Machine Learning and Counterfactual Explanations for Type 2 Diabetes Prediction and Prevention  \nTesi di Laurea Magistrale in  \nBiomedical Engineering-Ingegneria Biomedica  \nAuthor: Davide Console  \nStudent ID: 993506  \nAdvisor: Prof. Alessia Paglialonga  \nCo-advisor: Marta Lenatti  \nAcademic Year: 2022-23  \ni  \nAbstract  \nType 2 Diabetes Mellitus (T2DM) poses a significant and growing health challenge globally. Clinicians recognize prediabetes as an intermediate reversible state that precedes the onset of T2DM, offering a critical window for prevention. This study aims to leverage adataset extracted ad hoc from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) to develop a Machine Learning model capable of predicting not only the onset of T2DM but also the occurrence of prediabetes and normoglycemia.  \nThe model is trained on the total population (41888 records) and two stratifications: CurrentState = PD subgroup (19631 records), comprising patients with prediabetes atthe time of data acquisition, and CurrentState = NG subgroup (22257 records), consisting of patients with normoglycemia. Post hoc explainability techniques, including feature importance analysis, partial dependence plots, and waterfall plots for single instances, are employed to dissect the predictive models’ decision-making process. Additionally, counterfactual explanations are generated using the best-performing model to aid clinicians in devising personalized strategies to prevent T2DM progression while patients are still in the prediabetes state. Results demonstrate promising predictive performance, with an F1 Macro score of 83% for the total population, 81% for CurrentState = PD subgroup, and 58% for CurrentState = NG subgroup. Explainability analysis underscores the significance of Fasting Blood Sugar (FBS) and glycated hemoglobin (HbA1c), in the classification task, followed by Body Mass Index (BMI), High-Density Lipoprotein (HDL), and Low-Density Lipoprotein (LDL), while blood pressure has less impact on the models. Furthermore, counterfactual explanations, shed light on actionable interventions, with features such as BMI, HbA1c, FBS, and HDL emerging again as key factors for personalized prevention strategies across both the total population and CurrentState = PD subgroup.  \nOverall, this research underscores the potential of predictive modeling and explainable Artificial Intelligence in informing preventive interventions and personalized patient management strategies in the context of T2DM progression.  \nKeywords: Prediabetes, Type 2 Diabetes Mellitus, Machine Learning, Explainability, Counterfactual Explainability  \nAbstract in lingua italiana  \nIl diabete mellito di tipo 2 (T2DM) rappresenta una crescente e significativa sfida sanitaria globale. I medici riconoscono il prediabete (PD) come uno stato reversibile che precede l’insorgenza del T2DM, offrendo una finestra critica per la prevenzione. Questo studio utilizza un dataset estratto ad hoc dal Canadian Primary Care Sentinel Surveillance Network (CPCSSN) per sviluppare modelli di Machine Learning in grado di prevederenon solo l’insorgenza del T2DM, ma anche il verificarsi di PD e normoglicemia.  \nI modelli sono stati addestrati sulla popolazione totale (41888 record) e due suoi sottogruppi: il sottogruppo CurrentState = PD (19631 record), costituito da pazienti con PD al momento dell’acquisizione dei dati, e il sottogruppo CurrentState = NG (22257 record), costituito da pazienti con normoglicemia. Per analizzare il processo decisionale dei modelli predittivi sono state utilizzate tecniche di post-hoc explainability, tra cui analisi dell’importanza delle caratteristiche, grafici di dipendenza parziale e grafici a cascata. Inoltre, sono state generate spiegazioni controfattuali utilizzando il modello con le migliori prestazioni per aiutare i medici a ideare strategie personalizzate per prevenire la progressione del T2DM quando i pazienti sono anco","cbCaiaHsJCXDDwPA","https://ap.wps.com/l/cbCaiaHsJCXDDwPA","pdf",5085960,1,106,"English","en",105,"# Introduction\n## Diabetes Mellitus\n## Algorithms for prediction\n## Explainability in AI\n## Research Gap\n## Project Goal\n# Materials & Methods\n## CPCSSN Database description\n## Relevant features identification\n## Data extraction","[{\"question\":\"What clinical window does this research focus on for preventing T2DM?\",\"answer\":\"The work targets prediabetes as a reversible intermediate state before T2DM onset, using prediction to enable earlier preventive action.\"},{\"question\":\"Which dataset and labeling strategy are used to train the models?\",\"answer\":\"The models are trained on CPCSSN data, using the total population and stratified subgroups based on CurrentState = PD (prediabetes) and CurrentState = NG (normoglycemia).\"},{\"question\":\"How do explainability and counterfactual explanations support personalized prevention?\",\"answer\":\"Explainability identifies key predictive features such as FBS and HbA1c, while counterfactual explanations generate actionable feature changes (e.g., BMI, HbA1c, FBS, HDL) to guide individualized strategies.\"}]","Exploring Prediabetes Pathways - Using Machine Learning and Counterfactual Explanations for Type 2 Diabetes Prediction and Prevention | PDF",1785724368,267,{"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},"exploring-prediabetes-pathways-using-machine-learning-and-counterfactual-explanations-for-type-2-diabetes-prediction-and-prevention","",{"@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/exploring-prediabetes-pathways-using-machine-learning-and-counterfactual-explanations-for-type-2-diabetes-prediction-and-prevention/119455/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical window does this research focus on for preventing T2DM?","Question",{"text":75,"@type":76},"The work targets prediabetes as a reversible intermediate state before T2DM onset, using prediction to enable earlier preventive action.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and labeling strategy are used to train the models?",{"text":80,"@type":76},"The models are trained on CPCSSN data, using the total population and stratified subgroups based on CurrentState = PD (prediabetes) and CurrentState = NG (normoglycemia).",{"name":82,"@type":73,"acceptedAnswer":83},"How do explainability and counterfactual explanations support personalized prevention?",{"text":84,"@type":76},"Explainability identifies key predictive features such as FBS and HbA1c, while counterfactual explanations generate actionable feature changes (e.g., BMI, HbA1c, FBS, HDL) to guide individualized strategies.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]