[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120791-en":3,"doc-seo-120791-105":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},120791,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine Learning-Based Diabetes Prediction - Feature Analysis and Model Assessment","Machine learning is used to predict diabetes with a Kaggle dataset containing 13 clinical, demographic, and lifestyle features. A three-layer model attains 98.73% accuracy with an average error of 0.01%, demonstrating strong predictive performance. Feature importance analysis highlights Age, Gender, Polyuria, Polydipsia, visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, muscle stiffness, alopecia, genital thrush, weakness, and obesity as influential predictors. Results support early diabetes risk assessment and motivate further work to improve real-world generalizability.","Machine Learning-Based Diabetes Prediction: Feature Analysis  \nand Model Assessment  \nFares Wael Al-Gharabawi and Samy S. Abu-Naser  \nDepartment of Information Technology,  \nFaculty of Engineering and Information Technology,  \nAl-Azhar University, Gaza, Palestine  \nAbstract: This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98. 73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed to enhance model generalizability.  \nKeywords: Machine learning, Diabetes, Prediction  \nI. Introduction  \nDiabetes mellitus, a chronic metabolic disorder characterized by elevated blood glucose levels, poses a significant global health challenge. Timely diagnosis and accurate prediction of diabetes risk are paramount for effective prevention and management. Machine learning techniques have emerged as promising tools in this endeavor, allowing for the development of predictive models based on comprehensive datasets.  \nIn this study, we delve into the realm of diabetes prediction, leveraging the power of machine learning algorithms. Our dataset, acquired from Kaggle, encompasses 13 pertinent features, including clinical, demographic, and lifestyle factors. These features serve as the basis for constructing a predictive model designed to forecast diabetes incidence.  \nThe significance of our research lies not only in achieving a commendable accuracy rate of 98.73% but also in the identification of key features that substantially influence diabetes prediction. These findings have practical implications for healthcare practitioners, enabling them to proactively assess the risk of diabetes in individuals and implement timely interventions.  \nMoreover, this study contributes to the ongoing discourse in the field of diabetes prediction by addressing gaps in existing research and providing insights into feature importance. Nevertheless, it is essential to acknowledge the limitations of any predictive model and the necessity for further research to enhance its robustness and real-world applicability.  \nThis paper aims to serve as a valuable resource for researchers, clinicians, and policymakers interested in harnessing machine learning for diabetes prediction, emphasizing the potential of predictive modeling to make meaningful advancements in healthcare and public health initiatives.  \nII. Previous Studies  \nNumerous studies have explored the application of machine learning techniques for diabetes prediction. These studies have paved the way for our research, offering valuable insights and establishing a foundation for our work. Here, we provide a concise overview of some notable prior research in this domain:  \nSmith et al. (2022): Smith and colleagues employed a deep neural network to predict diabetes risk based on electronic health records. Their study highlighted the potential of deep learning models in capturing complex patterns in patient data.  \nBrown and Johnson (2022): Brown and Johnson utilized a random forest algorithm to predict diabetes onset in a large cohort of participants. Their research emphasized the importance of feature selection and model interpretability in diabetes prediction.  \nGupta et al. (2023): Gupta and co-authors conducted a comprehensive analysis of various machine learning algorithms, including logistic regression, support vector machines, and gradient boosting, for diabetes prediction. Their work provided valuable insights into the comparative performance of different algorithms.  \nChen et al. (2022): Chen and his team focused on feature engineering and selection techniqu","cbCaidotooEpkWjb","https://ap.wps.com/l/cbCaidotooEpkWjb","pdf",649881,1,9,"English","en",105,"# Introduction\n# Previous Studies\n# Machine Learning Approaches\n## Feature Importance Analysis","[{\"question\":\"What dataset and feature set are used for diabetes prediction?\",\"answer\":\"The study uses a Kaggle dataset with 13 features covering clinical, demographic, and lifestyle factors.\"},{\"question\":\"How accurate is the proposed three-layer model?\",\"answer\":\"The model achieves 98.73% accuracy and an average error of 0.01%.\"},{\"question\":\"Which features are identified as most influential predictors?\",\"answer\":\"Age, Gender, Polyuria, Polydipsia, visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, muscle stiffness, alopecia, genital thrush, weakness, and obesity are reported as influential predictors.\"}]","Machine Learning-Based Diabetes Prediction - Feature Analysis and Model Assessment | PDF",1785732052,23,{"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-based-diabetes-prediction-feature-analysis-and-model-assessment","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-diabetes-prediction-feature-analysis-and-model-assessment/120791/",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","2026-08-03",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},"What dataset and feature set are used for diabetes prediction?","Question",{"text":75,"@type":76},"The study uses a Kaggle dataset with 13 features covering clinical, demographic, and lifestyle factors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How accurate is the proposed three-layer model?",{"text":80,"@type":76},"The model achieves 98.73% accuracy and an average error of 0.01%.",{"name":82,"@type":73,"acceptedAnswer":83},"Which features are identified as most influential predictors?",{"text":84,"@type":76},"Age, Gender, Polyuria, Polydipsia, visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, muscle stiffness, alopecia, genital thrush, weakness, and obesity are reported as influential predictors.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]