[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124044-en":3,"doc-seo-124044-105":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},124044,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements - A concise study overview","Chronic kidney disease (CKD) remains often undetected until substantial kidney function loss occurs, making early screening a key driver of improved patient care. This study evaluates machine learning approaches to classify CKD and predict creatinine using three feature sets: at-home, monitoring, and laboratory. Artificial neural networks and random forests are trained on 400 patients with 25 inputs and assessed via 10-fold cross-validation using accuracy, sensitivity/specificity metrics, and regression error. Random forests achieve the strongest at-home CKD classification performance, with high accuracies also observed for monitoring and laboratory features, and feature importance highlighting hemoglobin, blood urea, hypertension, and diabetes mellitus.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMachine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements  \nBrady Metherall􀀍, Anna K. Berryman & Georgia S. Brennan  \nChronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role in effective management. Machine learning models demonstrate promise inCKD detection, yet the impact on detection and classification using different sets of clinical features remains under-explored. In this study, we focus on CKD classification and creatinine prediction using three sets of features:  \nat-home, monitoring, and laboratory. We employ artificial neural networks (ANNs) and random forests (RFs) on a dataset of 400 patients with 25 input features, which we divide into three feature sets. Using 10-fold cross-validation, we calculate metrics such as accuracy, true positive rate (TPR), true negative rate (TNR), and mean squared error. Our results reveal RF achieves superior accuracy (92.5%) in at-home CKD classification over ANNs (82.9%). ANNs achieve a higher TPR (92.0%), but a lower TNR (67.9%) compared with RFs (90.0% and 95.8%, respectively). For monitoring and laboratory features, both methods achieve accuracies exceeding 98%. The R2 score for creatinine regression is approximately 0.3 higher with laboratory features than at-home features. Feature importance analysis identifies the key clinical variables hemoglobin and blood urea, and key comorbidities hypertension and diabetes mellitus, in agreement with previous studies. Machine learning models, particularly RFs, exhibit promise in CKD diagnosis and highlight significant features in CKD detection. Moreover, such models may assist in screening a general population using at-home features—potentially increasing early detection of CKD, thus improving patient care and offering hope for a more effective approach to managing this prevalent health condition.  \nKeywords Chronic kidney disease classification, Creatinine prediction, Machine learning, At-home detection  \nChronic kidney disease (CKD) represents a global health challenge affecting millions worldwide and placing a substantial burden on healthcare systems1,2. More women are affected by CKD than breast cancer, and more men than prostate cancer3. CKD is often a silent and progressive condition remaining undetected until a significant loss of kidney function has occurred. Early detection and prediction are crucial for timely interventions and improved patient outcomes.  \nCKD is classified into five stages4–6 based on glomerular filtration rate (GFR)—a measure of kidney function. GFR measurement is complex and so is usually estimated using equations. The two most common equations are the Modification of Diet in Renal Disease (MDRD)7 and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)8 equations. The MDRD equation has limitations for healthy individuals or those with mild kidney dysfunction. In contrast, the CKD-EPI equation addresses some of these limitations, providing a more accurate estimate of GFR by adjusting for different creatinine ranges.  \nMachine learning, which has shown success in predicting diseases9, holds promise within nephrology10, including enhancing CKD screening and detection3, 11–13. One promising application of machine learning in the context of CKD is the potential for at-home detection or screening. Online CKD detection, such as through a health application on smartphones, is identified as an area for future research by Qezelbash-Cham et al.3. At-home CKD screening offers an ideal solution to the global health challenge posed by CKD. By leveraging user-friendly devices and predictive models, individuals could track key indicators of kidney health in the comfort of their homes. This approach not only increases early detection, but also enables a wider subset of  \nMathematical Institute, University of Oxford, Radcliffe Observatory Qu","cbCaidOTSaYy4EO0","https://ap.wps.com/l/cbCaidOTSaYy4EO0","pdf",2827471,1,11,"English","en",105,"# Introduction\n## Clinical challenge and need for early detection\n# Methods\n## Feature sets and machine learning models\n## Evaluation metrics and validation\n# Results\n## CKD classification performance across feature sets\n## Creatinine regression outcomes and feature importance\n# Discussion\n## Implications for at-home CKD screening and clinical management","[{\"question\":\"Which machine learning models are used to classify CKD and predict creatinine levels?\",\"answer\":\"The study applies artificial neural networks (ANNs) and random forests (RFs) to the same dataset for CKD classification and creatinine regression.\"},{\"question\":\"How are the input features organized for the experiments?\",\"answer\":\"Features are grouped into three sets: at-home measurements, monitoring features, and laboratory features, each reflecting different measurement accessibility.\"},{\"question\":\"What performance differences does the study find for at-home CKD classification?\",\"answer\":\"Random forests achieve higher at-home CKD classification accuracy (92.5%) than ANNs (82.9%), while ANNs show a higher true positive rate and a lower true negative rate compared with RFs.\"}]","Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements - A concise study overview | PDF",1785820071,28,{"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},"machine-learning-for-classifying-chronic-kidney-disease-and-predicting-creatinine-levels-using-at-home-measurements-a-concise-study-overview","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-classifying-chronic-kidney-disease-and-predicting-creatinine-levels-using-at-home-measurements-a-concise-study-overview/124044/",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},"Which machine learning models are used to classify CKD and predict creatinine levels?","Question",{"text":75,"@type":76},"The study applies artificial neural networks (ANNs) and random forests (RFs) to the same dataset for CKD classification and creatinine regression.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the input features organized for the experiments?",{"text":80,"@type":76},"Features are grouped into three sets: at-home measurements, monitoring features, and laboratory features, each reflecting different measurement accessibility.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance differences does the study find for at-home CKD classification?",{"text":84,"@type":76},"Random forests achieve higher at-home CKD classification accuracy (92.5%) than ANNs (82.9%), while ANNs show a higher true positive rate and a lower true negative rate compared with RFs.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]