[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126184-en":3,"doc-seo-126184-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126184,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Enhancing Transparency in Healthcare Machine Learning Models Using SHAP and DeepLIFT - A Methodological Approach","Enhancing transparency in healthcare machine learning models requires interpretable predictions that clinicians can trust and justify. This methodological study focuses on bridging technical model outputs with clinical applications by clarifying how models generate decisions in complex medical diagnoses. The approach uses SHapley Additive exPlanations (SHAP) and DeepLIFT to distribute comprehension of decision pathways and quantify feature contributions. Experiments highlight explainability and trustworthiness improvements for melanoma prediction and diabetic retinopathy diagnosis, supporting reliable, transparent decision making for medical specialists.","ENHANCING TRANSPARENCY IN HEALTHCARE MACHINE LEARNING MODELS USING SHAP AND DEEPLIFT A METHODOLOGICAL APPROACH  \nSeyedamir Shobeiri 1  \n1 Department of Computer Science, University of Luxembourg. Luxembourg  \n[seyedamir.shobeiri.001@student.uni.lu](seyedamir.shobeiri.001@student.uni.lu1)[1](seyedamir.shobeiri.001@student.uni.lu1)  \nCorresponding Author: Seyedamir Shobeiri  \nReceived:30/05/2024 ; Revised:27/07/2024; Accepted:14/08/2024  \nDOI:10.31987/ijict.7.2.285  \nAbstract- This paper intends to provide a better understanding of how these models produce predictions, particularly in complex medical diagnoses, and at the same time bridging the gap between technical model outputsand clinical applications. This study addresses the critical problem of transparency of machine learning (ML) models in health care where interpretability is an essential aspect for ethical decision making and trust building.The goal of this paper is to present a clearer understanding of how these models generate predictions, especially in important fields like complex medical diagnoses, thereby bridging the gap between technical model outputs and clinical applications. The crucial issue of transparency in machine learning (ML) models within healthcare is addressed in this study where interpretability plays a vital role in ethical decision-making and fostering trust. The focus of the research is enhancing model transparency by using SHapley Additive exPlanations (SHAP) and Deep Learning Important FeaTures (DeepLIFT), two crucial methods that are designed to elucidate the decision-making processes of ML models.This mode of approach helps to have more distributed comprehension of the decision pathways by models thus aiding in knowing how each feature contributed to the last prediction. It is this method that has been employed to showcase the efficiency of predicting melanoma and also diabetic retinopathy which are two vital medical diagnostic areas. In healthcare, SHAP along with DeepLIFT has improved the models’ explainability and trustworthiness significantly and hence making them easy for those in the field. The advanced interpretability methods presented in this document enhances ML model transparency especially when dealing with health issues. As a result, interpretability becomes an even bigger issue and they are supposed to be able to use these tools for reliable and open decisions when it comes to medical specialists.  \nkeywords: Healthcare AI, SHAP, Deep LIFT, Model Transparency.  \nI. INTRODUCTION  \nArtificial intelligence and machine learning have become integrated in healthcare systems and the improvements since the implementation of the technologies has been impressive. AI systems in different fields especially using the ML techniques have been responsive in analyzing large datasets and data in the medical field and has helped in analyzing various data that a doctor might not see normally and help in early detection of diseases like cancer and Diabetic retinopathy. However, as these models become more sophisticated and complex, a critical issue arises: the fact that many ML models are initially \"black boxes\" and that the decision-making internal to the algorithm is not transparent to an external viewer. One major difficulty implemented when models do not provide clear decision, logic, or understanding is in the field of medical healthcare where exact explanation about diagnosis or prescription is required by both doctors and patients. Some of situational and professional implications are as follows: Medical professionals are not only expected to make correct diagnostic decisions but also to justify them to the patient. This may be an issue in practice due to the ’black box’ nature of many ML models; they can contribute to this trust but at the same time decision making is not clear to healthcare providers or explainable to others. This issue is most crucial in areas considered as having high risk whereby any single mistake could lead to  \nThis ","cbCaifc5Ad1iXgmH","https://ap.wps.com/l/cbCaifc5Ad1iXgmH","pdf",2347796,9,1,17,"English","en",105,"# Introduction\n## The Transparency Challenge in Healthcare ML\n## Interpretability Methods: SHAP and DeepLIFT\n## Proposed Integration for Clinical Use","[{\"question\":\"Why is transparency a critical issue for healthcare machine learning models?\",\"answer\":\"Many ML models behave as black boxes, so their decision logic is not transparent to clinicians and patients. In healthcare, lack of clear explanations complicates justification of diagnoses and may be especially risky in high-stakes decisions.\"},{\"question\":\"How does SHAP improve interpretability in this methodology?\",\"answer\":\"SHAP provides a unique measure of feature significance based on cooperative game theory. It helps translate model predictions into understandable contributions, indicating which factors influence a decision.\"},{\"question\":\"What role does DeepLIFT play alongside SHAP?\",\"answer\":\"DeepLIFT propagates contribution scores through network layers by comparing neuron outputs to a reference input, highlighting sensitivity to feature changes. Combined with SHAP, it supports more comprehensive layer-aware explanation of model behavior for healthcare tasks.\"}]","Enhancing Transparency in Healthcare Machine Learning Models Using SHAP and DeepLIFT - A Methodological Approach | PDF",1785903673,43,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"enhancing-transparency-in-healthcare-machine-learning-models-using-shap-and-deeplift-a-methodological-approach","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/enhancing-transparency-in-healthcare-machine-learning-models-using-shap-and-deeplift-a-methodological-approach/126184/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is transparency a critical issue for healthcare machine learning models?","Question",{"text":77,"@type":78},"Many ML models behave as black boxes, so their decision logic is not transparent to clinicians and patients. In healthcare, lack of clear explanations complicates justification of diagnoses and may be especially risky in high-stakes decisions.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does SHAP improve interpretability in this methodology?",{"text":82,"@type":78},"SHAP provides a unique measure of feature significance based on cooperative game theory. It helps translate model predictions into understandable contributions, indicating which factors influence a decision.",{"name":84,"@type":75,"acceptedAnswer":85},"What role does DeepLIFT play alongside SHAP?",{"text":86,"@type":78},"DeepLIFT propagates contribution scores through network layers by comparing neuron outputs to a reference input, highlighting sensitivity to feature changes. Combined with SHAP, it supports more comprehensive layer-aware explanation of model behavior for healthcare tasks.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,122,125,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":120,"slug":121},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":123,"slug":124},30,"research-report",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]