[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127314-en":3,"doc-seo-127314-105":31,"detail-sidebar-cat-0-en-105":92},{"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},127314,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786593998035447633",8,"Research & Report","Data to Diagnosis - Evaluating Machine Learning Algorithms for Predictive Healthcare in Diabetes","Diabetes mellitus is a chronic metabolic disease that requires early diagnosis to prevent serious complications and reduce long-term health risks. This research evaluates eight machine learning algorithms—SVM, XGBoost, Naive Bayes, Logistic Regression, Gradient Boosting, KNN, Decision Tree, and Random Forest—using a formatted dataset with clinical and demographic attributes. Preprocessing includes normalization and categorical encoding. Models are assessed via accuracy, precision, recall, F1-score, and confusion matrices. Despite the imbalanced dataset (about 10% diabetic cases) and without class balancing or hyperparameter tuning, ensemble methods, especially Gradient Boosting and XGBoost, achieve over 91% accuracy, indicating promise for early diabetes prediction.","Keywords: Diabetes Prediction, Machine Learning, Early Diagnosis, Clinical Data Analysis.  \nJournal Info:  \nSubmitted: May 13, 2025  \nAccepted: July 23, 2025  \nPublished: July 31, 2025  \nData to Diagnosis: Evaluating Machine Learning Algorithms for Predictive Healthcare in Diabetes  \nMusharaf Ali Talpur, 1 , Manal A. Asiri 2 , Umme Laila Khaliq3 , Muhammad Noman Saeed5*  \n3  \n,  \nSamar Raza Talpur  \n4  \n,  \nAbdul  \n1Abida Taherani Sindh Development Studies Centre (ATSDSC), University of Sindh, Jamshoro, Pakistan; 2Aseer Health Cluster, Health Programs Department, Ministry of Health, Saudi Arabia; 3 Computer Science Department, Institute of Business Management (IoBM), Karachi, Pakistan;  \n4 ICT Department Sukkur IBA University, Sukkur, Pakistan; 5 E-Learning Center, Jazan University, Jazan, Saudi Arabia  \nAbstract  \nDiabetes mellitus, a chronic metabolic disease, presents alarming challenges to world health. It is vital to diagnose it early to prevent serious complications. In this research, eight machine learning algorithms—SVM, XGBoost, Naive Bayes, Logistic Regression, Gradient Boosting, KNN, Decision Tree, and Random Forest—are used on a formatted dataset with clinical and demographic attributes. Normalization and categorical encoding were done for preprocessing. Although no class-balancing methods (e.g., SMOTE or weighting) were used or hyperparameter tuning was performed, models were tested with accuracy, precision, recall, F1-score, and confusion matrices. Interestingly, the dataset is very imbalanced ( 10% diabetic cases), and thus may influence sensitivity. Ensemble models, particularly Gradient Boosting and XGBoost, reported more than 91% accuracy. In spite of limitations, findings suggest the promise of ML in early prediction of diabetes.  \n*Correspondence author email [address:](address: msaeed@jazanu.edu.sa)[ msaeed@jazanu.edu.sa](address: msaeed@jazanu.edu.sa)[ ](address: msaeed@jazanu.edu.sa)DOI: 10.21015/vtse.v13i3 .2141  \n1 Introduction  \nChronic diseases are persistent conditions with lasting effects or permanent damage [1, 2] . Such diseases tend to negatively influence quality of life and account for a high percentage of national healthcare expenditure [3, 4] . Among such diseases, diabetes is a  \nsignificant health risk, and its contribution to medical deaths grows each year. It is an increasing problem in developing and developed countries [5, 6].If unchecked, diabetes, a chronic condition brought on by elevated blood sugar levels, can damage multiple organs[29, 31] . Diabetes is a long-term medical condi-  \nThis work is licensed under a Creative Commons Attribution 3.0 License.  \nVFAST Transactions on Software Engineering Volume 13, Issue 3, 2025  \ntion that affects how well our bodies use food as fuel [30] .  \nDiabetes is related to high blood glucose levels. The insulin hormone, secreted by beta cells in the pancreas, allows glucose to enter the bloodstream. Lack of insulin production causes diabetes [7] . Diabetes may lead to a higher need for drink and increased hunger, heart disease, and renal complications, and could even be life-threatening if not treated [8, 9] .  \nDiabetes can be categorised into two types: Type 1 and Type 2 . Type 1 diabetes is common among people under the age of 30 and involves symptoms like increased thirst, increased blood sugar, and increased urination [10] . Type 2 diabetes usually occurs among middle-aged and older people and is generally associated with obesity, hypertension, dyslipidemia, and atherosclerosis. It requires medication in combination with insulin injections [11, 12] . While there is no cure, early detection and treatment greatly simplify the process and enhance prognosis. Thus, diabetes prediction has become a topic of significant medical interest [10–13] .  \nMajor advancements have been made in the research of disease forecasting techniques for disorders like diabetes and COVID-19 [14, 15] . Concurrent advances have been made within machine learning (ML) since ","cbCaigZl3SvTIZG7","https://ap.wps.com/l/cbCaigZl3SvTIZG7","pdf",360923,2,1,13,"English","en",105,"# Introduction\n## Diabetes background and clinical relevance\n## Machine learning for disease forecasting\n## Prior work on diabetes prediction methods","[{\"question\":\"Which machine learning algorithms are evaluated for diabetes prediction?\",\"answer\":\"The study evaluates eight algorithms: SVM, XGBoost, Naive Bayes, Logistic Regression, Gradient Boosting, KNN, Decision Tree, and Random Forest.\"},{\"question\":\"How was the dataset prepared before training the models?\",\"answer\":\"The dataset underwent normalization and categorical encoding as part of preprocessing.\"},{\"question\":\"What evaluation metrics and results are reported?\",\"answer\":\"Models are tested using accuracy, precision, recall, F1-score, and confusion matrices; ensemble models like Gradient Boosting and XGBoost report more than 91% accuracy.\"}]","Data to Diagnosis - Evaluating Machine Learning Algorithms for Predictive Healthcare in Diabetes | PDF",1785938250,33,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"data-to-diagnosis-evaluating-machine-learning-algorithms-for-predictive-healthcare-in-diabetes","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/data-to-diagnosis-evaluating-machine-learning-algorithms-for-predictive-healthcare-in-diabetes/127314/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which machine learning algorithms are evaluated for diabetes prediction?","Question",{"text":76,"@type":77},"The study evaluates eight algorithms: SVM, XGBoost, Naive Bayes, Logistic Regression, Gradient Boosting, KNN, Decision Tree, and Random Forest.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the dataset prepared before training the models?",{"text":81,"@type":77},"The dataset underwent normalization and categorical encoding as part of preprocessing.",{"name":83,"@type":74,"acceptedAnswer":84},"What evaluation metrics and results are reported?",{"text":85,"@type":77},"Models are tested using accuracy, precision, recall, F1-score, and confusion matrices; 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