[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125810-en":3,"doc-seo-125810-105":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},125810,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Improving Performance for Diabetic Nephropathy Detection Using Adaptive Synthetic Sampling Data in Ensemble Method of Machine Learning Algorithms","Diabetic nephropathy is a severe diabetic complication that threatens kidney function and can progress to renal failure, making early, accurate prediction essential for timely intervention and better health outcomes. This study trains predictive models using 500 medical record datasets with imbalanced classes. It proposes adaptive synthetic sampling (ADASYN) integrated with ensemble machine learning methods such as Random Forest and AdaBoost to improve minority-class representation. Experiments show an improved 4% gain in precision, recall, accuracy, and F1-score, enhancing accuracy and robustness.","Improving Performance for Diabetic Nephropathy Detection Using Adaptive Synthetic Sampling Data in Ensemble Method of Machine  \nLearning Algorithms  \nLailil Muflikhah 1, Fitra A. Bachtiar 1, Dian Eka Ratnawati2, Riski Darmawan 1  \n1Department of Informatics Engineering, Brawijaya University, Malang 65145, Indonesia  \n2Department of Information System, Brawijaya University, Malang 65145, Indonesia  \nARTICLE INFO  \nArticle history:  \nReceived December 30, 2023 Revised February 18, 2024 Published March 09, 2024  \nKeywords:  \nNephropathy; Oversampling; Adasyn;  \nBagging;  \nBoosting;  \nMachine learning  \nCorresponding Author:  \nABSTRACT  \nNephropathy is a severe diabetic complication affecting the kidneys that presents a substantial risk to patients. It often progresses to renal failure and other critical health issues. Early and accurate prediction of nephropathy is paramount for effective intervention, patient well-being, and healthcare resource optimization. This research used medical records from 500 datasets of diabetic patients with imbalanced classes. The main goal ofthis study is to get high-performance predictive models for nephropathy. So, this study suggests a new way to deal with the common problem of having too little or too much data when trying to predict nephropathy: adding more data through adaptive synthetic sampling (ADASYN) . This technique is particularly pertinent in ensemble machine-learning methods like Random Forest, AdaBoost, and bagging (Adabag) . By increasing the number of instances of minority classes, it tries to reduce the bias that comes with imbalanced datasets, which should lead to more accurate and strong predictive models in the long run. The experimental results show an improving 4% rise in performance evaluation such as precision, recall, accuracy, and f1-score, especially for the ensemble methods. Two contributions of this research are highlighted here: first, the utilization of adaptive synthetic sampling data to improve the balance and diversity of the training dataset. The second contribution is incorporating ensemble methods within machine learning algorithms to enhance the accuracy and robustness of diabetic nephropathy detection.  \nThis work is licensed under a Creative Commons Attribution-Share Alike 4.0  \nLailil Muflikhah, Department of Informatics Engineering, Brawijaya University, Malang, 65145, Indonesia Email: [lailil@ub.ac.id](lailil@ub.ac.id)  \n1. INTRODUCTION  \nDiabetic nephropathy (DN) is a severe complication of diabetes mellitus and a leading cause of end-stage renal disease. Although albuminuria is a marker of DN, there is a subset of DN patients who are not characterized by high albuminuria levels. This poses a significant challenge in the early detection of the disease. Furthermore, the disease is characterized by different pathophysiological mechanisms and clinical outcomes. Hence the need for case-specific biomarkers and diagnostic criteria for personalized treatment strategies. In addition, current treatment approaches mainly target albuminuric DN, may adjust in the choice of personalized therapy to DN without albuminuria. There is thus a need to identify novel biomarkers, and develop targeted interventions to improve the clinical outcomes ofthis often overlooked subset of DN patients. In this study, we aim to explore the challenges and opportunities in early identification and prediction of albumin level in diabetic patients as a predictor of diabetic nephropathy. Furthermore, potential research directions may address this unmet medical need [1] . On the other hand, diabetic nephropathy is a kidney condition that results from diabetes. Europe and the United States have the highest rates of kidney failure. Diabetic nephropathy  \nprogresses through five distinct stages. In the early stage (Phase I), hyperfiltration occurs, leading to increased GFR (glomerular filtration rate), AER (albumin excretion rate), and kidney enlargement. In Phase II, albumin excretion remains r","cbCaicJpPjyWpJ9E","https://ap.wps.com/l/cbCaicJpPjyWpJ9E","pdf",932726,1,15,"English","en",105,"# Introduction\n## Diabetic nephropathy and early detection challenges\n## Machine learning for medical diagnosis","[{\"question\":\"Why is early detection of diabetic nephropathy important?\",\"answer\":\"Diabetic nephropathy can progress to severe kidney impairment and end-stage renal disease. Early and accurate prediction enables timely intervention and better allocation of healthcare resources.\"},{\"question\":\"How does ADASYN help with imbalanced datasets in this study?\",\"answer\":\"ADASYN increases the number of instances in minority classes to reduce bias caused by imbalance. This helps produce more accurate and stronger predictive models.\"},{\"question\":\"Which ensemble machine learning methods are used to improve detection performance?\",\"answer\":\"The study applies ensemble methods such as Random Forest and AdaBoost, including bagging-based variants, to enhance accuracy and robustness for diabetic nephropathy detection.\"}]","Improving Performance for Diabetic Nephropathy Detection Using Adaptive Synthetic Sampling Data in Ensemble Method of Machine Learning Algorithms | PDF",1785901329,38,{"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},"improving-performance-for-diabetic-nephropathy-detection-using-adaptive-synthetic-sampling-data-in-ensemble-method-of-machine-learning-algorithms","",{"@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/improving-performance-for-diabetic-nephropathy-detection-using-adaptive-synthetic-sampling-data-in-ensemble-method-of-machine-learning-algorithms/125810/",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","2026-08-05",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},"Why is early detection of diabetic nephropathy important?","Question",{"text":75,"@type":76},"Diabetic nephropathy can progress to severe kidney impairment and end-stage renal disease. Early and accurate prediction enables timely intervention and better allocation of healthcare resources.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does ADASYN help with imbalanced datasets in this study?",{"text":80,"@type":76},"ADASYN increases the number of instances in minority classes to reduce bias caused by imbalance. This helps produce more accurate and stronger predictive models.",{"name":82,"@type":73,"acceptedAnswer":83},"Which ensemble machine learning methods are used to improve detection performance?",{"text":84,"@type":76},"The study applies ensemble methods such as Random Forest and AdaBoost, including bagging-based variants, to enhance accuracy and robustness for diabetic nephropathy detection.","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"]