[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118373-en":3,"doc-seo-118373-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118373,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Automatic Diabetes Prediction with Explainable Machine Learning Techniques","Diabetes is a metabolic disorder in which impaired insulin processing leads to elevated blood glucose levels, making early diagnosis essential to reduce downstream complications. This work applies machine learning to predict diabetes presence in female patients at an early stage using both the Pima Indian dataset and a private dataset from a local Bangladeshi hospital. Feature scaling, SMOTE synthetic oversampling, and GridSearchCV hyperparameter optimization enhance model performance. A support vector machine using SMOTE achieves 87% accuracy and 91% F1 score, improving to 95% accuracy after optimization, while LIME explains SVM predictability.","Automatic diabetes prediction with explainable machine  \nlearning techniques  \nAdiba Haque, Sanjida Islam, Nusrat Rahim Mim, Sabrina Mannan Meem, Ananya Saha, Riasat Khan  \nDepartment of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh  \nArticle history:  \nReceived Mar 22, 2024 Revised Jul 7, 2024 Accepted Jul 26, 2024  \nKeywords:  \nArtificial intelligence  \nDiabetes prediction Explainable artificial intelligence Machine learning  \nMetabolic disorder  \nSynthetic oversampling technique  \nCorresponding Author:  \nDiabetes is a metabolic disorder caused by various genetic, physiological and behavioral factors. It occurs due to an imbalance in the body’s insulin processing, which results in elevated blood sugar levels. Its early diagnosis can alleviate the risk of other deadly diseases. The onset and accurate detection of diabetes can decrease the progression of different complications and dysfunction of tissues. The principal objective of this article is to utilize machine learning approaches to predict the existence of diabetes in female patients at a primary stage. Multiple machine learning, including ensemble classifiers with the Pima Indian dataset and a private dataset obtained from a local Bangladeshi hospital, are used in this work. We employed feature scaling, synthetic oversampling technique (SMOTE), and hyperparameter optimization with GridSearchCV to get the best performance from different machine learning algorithms. The support vector machine (SVM) with the SMOTE framework and default hyperparameters achieved the accuracy and F1 score of 87% and 91%, respectively. The accuracy and F1 score of the SVM model improved to 95% and 91%, respectively, with hyperparameter optimization. Finally, explainable artificial intelligence with the local interpretable model-agnostic explanations (LIME) is employed to illustrate the predictability of the SVM technique.  \nThis is an open access article under the CC BY-SA license.  \nRiasat Khan  \nDepartment of Electrical and Computer Engineering, North South University Plot: 15, Block: B, Bashundhara, Baridhara, Dhaka-1229, Bangladesh  \nEmail: [riasat.khan@northsouth.edu](riasat.khan@northsouth.edu)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nWhen the body’s insulin is used inadequately, and consequently, the pancreas fails to generate adequate insulin, then an immedicable disease occurs known as diabetes [1] . There are different types of diabetes characterized by hyperglycemia, for instance, type one, type two, and gestational diabetes. Prediabetes is also considered another type of diabetes. Sometimes people have a glucose level that is more excess than standard but not that much excess to type 2 diabetes. This condition is called prediabetes [2] . Worldwide 537 million individuals aged from 20 to 79 years have been affected by diabetes, according to a report by World Health Organization (WHO) published in 2021. The information anticipated that by 2030 and 2045, the rate would be increased to 643 million and 783 million, respectively [2] . In 2019, approximately 8.40 million adults had diabetes in Bangladesh, which is anticipated to expand to almost 15 million by 2045. 3.80 million people were expected to have prediabetes in 2019. 8.2% of rural women and 12.9% of females in urban areas of Bangladesh are affected by gestational diabetes mellitus [3] . The treatment of diabetes varies in steps, with what amount of insulin the body makes and how properly the body can use available insulin. Diabetes is not curable, yet it is controllable. Diabetes care is associated with adopting a healthy lifestyle, restricted diet, weight control and regular physical activity.  \nAccurate and prompt prediction of diabetes is a concern. Notable works have been done on the automatic identification of diabetes utilizing various machine learning approaches. The automated prediction of these works is expected to come up with a helpful referencing tool and preliminary judgment for ","cbCaitRDwpHBkEe1","https://ap.wps.com/l/cbCaitRDwpHBkEe1","pdf",417088,1,"English","en",105,"# Introduction\n## Related Work\n## Proposed Methodology\n## Experiments and Results\n## Explainability with LIME","[{\"question\":\"What problem does this study address?\",\"answer\":\"The study focuses on early and accurate prediction of diabetes in female patients to support timely clinical decision-making and reduce healthcare workload.\"},{\"question\":\"Which datasets are used for training and evaluation?\",\"answer\":\"It uses the Pima Indian dataset and an additional private dataset collected from a local Bangladeshi hospital.\"},{\"question\":\"How does the approach improve model performance?\",\"answer\":\"It applies feature scaling, SMOTE for synthetic oversampling, and hyperparameter optimization using GridSearchCV to obtain better results.\"}]","Automatic Diabetes Prediction with Explainable Machine Learning Techniques | PDF",1785683323,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"automatic-diabetes-prediction-with-explainable-machine-learning-techniques","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/automatic-diabetes-prediction-with-explainable-machine-learning-techniques/118373/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does this study address?","Question",{"text":74,"@type":75},"The study focuses on early and accurate prediction of diabetes in female patients to support timely clinical decision-making and reduce healthcare workload.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which datasets are used for training and evaluation?",{"text":79,"@type":75},"It uses the Pima Indian dataset and an additional private dataset collected from a local Bangladeshi hospital.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the approach improve model performance?",{"text":83,"@type":75},"It applies feature scaling, SMOTE for synthetic oversampling, and hyperparameter optimization using GridSearchCV to obtain better results.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]