[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126152-en":3,"doc-seo-126152-105":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},126152,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Comparative Analysis of Machine Learning Algorithms to Predict Type II Diabetes","Machine Learning (ML) models are increasingly accurate as training data grows in both size and quality, yet healthcare disease diagnosis, detection, and prediction remains challenging and computationally demanding. This study conducts experiments on the Pima Indian Diabetes Dataset (PIDDS) in two stages to compare supervised algorithms. It evaluates prediction accuracy and efficiency while testing whether simpler models can perform competitively. Preprocessing including imputation, feature selection, scaling, and discretization is applied to improve classification performance.","Comparative Analysis of Machine Learning Algorithms to Predict  \nType II Diabetes  \nPuneet Misra, Arun Singh Yadav  \nDepartment of Computer Science University of Lucknow Lucknow, India  \n[puneetmisra@gmail.com](puneetmisra@gmail.com),  \n[arun.ai.lkouniv@gmail.com](arun.ai.lkouniv@gmail.com)  \n This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nAbstract— Machine Learning (ML) models are becoming robust and more accurate nowadays as the rapid increase in the amount and quality of training data. Researchers are proposing complex models for real-life problems to achieve higher accuracy, which requires high computing and other resources. In the context of the healthcare disease diagnosis, detection and prediction is still a challenge. Early diagnosis of a disease or ailment helps in timely recovery. Moreover, health been core to every individual, a lot of work is being done in this field to improve upon by using all available information. Current paper experiments on Pima Indian Diabetes Dataset (PIDDS) in two stages A and B. The main objective of this study is to review the accuracy of the applied machine learning algorithms and analyze their efficiency in predictions. Another essential objective is to show the efficacy of simpler models. Fields like computer vision and NLP have given rise to deep learning with complex and high computational models setting the trend to apply them in almost all the fields While they help where we have an abundance of data and complex relationships, simpler models still can do wonders and on their day can challenge these behemoths. We have also applied preprocessing methods (imputation, feature selection, scaling and discretization) to improve the classification accuracy. The algorithms selected for this problem are Logistic regression (LR), Artificial Neural Networks (ANN), Support Vector Machine(SVM), Naïve Bayes (NB), and Decision Tree(DT) . LR provided the best accuracy, and the rest ofthe models are very close to each other.  \nKeywords—Machine learning, Disease prediction, classification, Preprocessing, Logistic Regression, ANN, Naïve Bayes, SVM, Decision Tree.  \n1. Introduction  \nIntelligent learning for prediction and forecasting is the topic that is under consideration intoday's promising research related to Artificial Intelligence(AI) . Learning is the critical requirement for any intelligent behavior. Researchers have agreed that without learning, there is  \nno intelligence. Therefore, machine learning has become a rapidly developing subfield of AIresearch. These intelligent algorithms were from the very beginning designed and used to analyze medical, clinical information[1] . Machine learning algorithms analyze the historical data and extract the useful and not so apparent patterns from the dataset for prediction and diagnosis[2][3] . Challenges with medical data is that it is non-linear, heterogeneous, and noisy[4] . So that information needs to be preprocessed to get the better result. Diabetes is a severe health problem in which the amount of sugar content cannot be regulated. Type I diabetes is caused when the human body refused to produce insulin. Type II diabetes makes the human body insulin resistance that causes other serious complications. Thus, the early and timely diagnosis of diabetes may prevent serious complications. The various machine learning-based system has been developed in recent years to predict diabetes[5][6] still scientists and medical experts evolving new and intelligent algorithms and proved that machine learning algorithms[7][8] performed better in disease diagnosing. The capability to work on extensive, heterogeneous data taken from different sources and keep improving the model performance by adding the background details to make it a more powerful tool[9] . The onl","cbCaienEWRIlz5Rq","https://ap.wps.com/l/cbCaienEWRIlz5Rq","pdf",826949,9,1,15,"English","en",105,"# Introduction\n# Review of Literature","[{\"question\":\"What is the main objective of this study on Type II diabetes prediction?\",\"answer\":\"The study compares the accuracy and efficiency of multiple supervised machine learning algorithms for Type II diabetes prediction and evaluates how well simpler models perform.\"},{\"question\":\"Which dataset is used for the experiments, and what are the stages mentioned?\",\"answer\":\"The experiments use the Pima Indian Diabetes Dataset (PIDDS) and are conducted in two stages labeled A and B.\"},{\"question\":\"What preprocessing methods are applied to improve classification accuracy?\",\"answer\":\"The paper applies preprocessing methods including imputation, feature selection, scaling, and discretization to improve classification accuracy.\"}]","Comparative Analysis of Machine Learning Algorithms to Predict Type II Diabetes | PDF",1785903424,38,{"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},"comparative-analysis-of-machine-learning-algorithms-to-predict-type-ii-diabetes","",{"@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/comparative-analysis-of-machine-learning-algorithms-to-predict-type-ii-diabetes/126152/",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","2026-08-25","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},"What is the main objective of this study on Type II diabetes prediction?","Question",{"text":77,"@type":78},"The study compares the accuracy and efficiency of multiple supervised machine learning algorithms for Type II diabetes prediction and evaluates how well simpler models perform.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which dataset is used for the experiments, and what are the stages mentioned?",{"text":82,"@type":78},"The experiments use the Pima Indian Diabetes Dataset (PIDDS) and are conducted in two stages labeled A and B.",{"name":84,"@type":75,"acceptedAnswer":85},"What preprocessing methods are applied to improve classification accuracy?",{"text":86,"@type":78},"The paper applies preprocessing methods including imputation, feature selection, scaling, and discretization to improve classification accuracy.","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"]