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The approach uses two phases: hyperparameter tuning with six algorithms and selection of top models for a voting classifier, achieving 98.69% accuracy with Random Forest via a simple GUI.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/predict-diabetes-using-voting-classifier-and-hyper-tuning-technique/324426/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/predict-diabetes-using-voting-classifier-and-hyper-tuning-technique/324426.png","ImageObject",300,407,{"name":42,"@type":43},"Anda","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-21","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",5,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why is diabetes early prediction important in the proposed study?","Question",{"text":63,"@type":64},"The study highlights that many people are unaware of early-stage diabetes symptoms, and delayed detection increases the likelihood of severe complications such as heart attack, kidney issues, and blindness.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What dataset is used for diabetes prediction?",{"text":68,"@type":64},"The framework uses the Pima Indian Diabetes Dataset (PIDD) in CSV format, containing 768 records.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the model combine different algorithms to improve prediction?",{"text":72,"@type":64},"It first applies six classification algorithms with hyperparameter optimization (Randomized Search and TPOT), then selects the four best-performing models and feeds them into a voting classifier. 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Additionally, the majority of people are not aware of the early-stage diabetes symptoms. The above reasons encourage developing a diabetes prediction system using machine learning techniques. The Pima Indian Diabetes Dataset (PIDD) was utilized for this framework as it is common and appropriate dataset in CSV format. In addition, the proposed framework is divided into two phases of model selection. In the first phase, six different algorithms namely, Logistic Regression, Decision Tree, Random Forest, K-nearest neighbor, Support Vector Machine and Naïve Bayes are applied, then two different hyper parameter techniques namely, Randomized Search and TPOT (autoML) are used to increase the accuracy level for each algorithm. In the second phase, the four best performed algorithms with best estimated parameters are chosen and used as input for the voting classifier. The results show that the Random Forest is the best performed performance algorithm to predict diabetes via a simple graphic user interface with 98.69% accuracy level. |\n| --- | --- |\n| Keywords:\u003Cbr>Diabetic Prediction, Graphical User Interface, Hyper Tuning,Machine Learning Algorithm,Voting Classifier. |  |\n\n1. INTRODUCTION  \nBlood is one of the most vital place which includes many complex processes relevant to human’s food, e.g., transferring carbohydrates to energy. When a piece of carbohydrate is eaten, the blood converts it to glucose (i.e., sugar) and carries the glucoses as a source of energy for all body cells. One of the major types of sugar is blood glucose; it is the main source of energy at the same time [1] . Moreover, insulin is a hormone that is produced by pancreas allowing sugar to enter into the cells and make organs work properly (see Figure 1) . In addition, there is a health condition called a diabetes mellitus which occurs when pancreas does not make enough insulin and/or cells do not adjust properly to insulin and this results in having too much glucose in blood [2] .  \nFigure 1: Work of Pancreas.  \nArtificial Intelligence (henceforth AI) is a common field in computer science; it is a rule-based and operated by complicated statistical algorithms [3] . One of the vital part of AI is machine learning (henceforth ML) and any computer systems and/or applications are developed using ML that have capability to automatically learn from available data or experiences in the absence of writing code for each case [4] . There are many ML types but two major kinds are supervised and unsupervised learning and both categories utilize available datasets in order to improve data understanding and realize valuable knowledge [5] . ML algorithms are considerably applying in many scientific fields and have role in uprising businesses in the world. For example, for diabetes early prediction, risk management and diabetic diagnosis, ML is used. In other words, in the past ten years, ML based system is developed to works on managing diabetes as a chronic diseases and providing clinical decision support [6] .  \nThere is no doubt that people’s lifestyle has been changed due to industrialization and globalization and as a result they prefer to consume sugary food, choose a sedentary work and ends with increasing diabetes [7] . Diabetes is multifunctional di","cbCaidanQK52tAze","https://ap.wps.com/l/cbCaidanQK52tAze","pdf",879157,16,"English","# Abstract\n# Keywords\n# 1. INTRODUCTION","[{\"question\":\"Why is diabetes early prediction important in the proposed study?\",\"answer\":\"The study highlights that many people are unaware of early-stage diabetes symptoms, and delayed detection increases the likelihood of severe complications such as heart attack, kidney issues, and blindness.\"},{\"question\":\"What dataset is used for diabetes prediction?\",\"answer\":\"The framework uses the Pima Indian Diabetes Dataset (PIDD) in CSV format, containing 768 records.\"},{\"question\":\"How does the model combine different algorithms to improve prediction?\",\"answer\":\"It first applies six classification algorithms with hyperparameter optimization (Randomized Search and TPOT), then selects the four best-performing models and feeds them into a voting classifier. The best result is reported with Random Forest at 98.69% accuracy.\"}]","Predict Diabetes Using Voting Classifier and Hyper Tuning Technique | PDF",1789967899]