[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119354-en":3,"doc-seo-119354-105":30,"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":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},119354,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Comparative Analysis of Supervised Machine Learning Algorithms for Diabetes Prediction","Diabetes mellitus is a chronic metabolic disorder marked by persistent hyperglycemia, and its growing prevalence creates major public-health risks worldwide. Accurate, timely prediction is essential for prevention and effective management. This study builds a supervised machine learning predictive model to forecast diabetes using Decision Tree, Naïve Bayes, Artificial Neural Network, and Logistic Regression. Model comparison is conducted using performance metrics including accuracy, recall, precision, and F-score, supporting a data-driven selection of suitable algorithms for diabetes prediction.","Comparative Analysis of Supervised Machine Learning Algorithms for Diabetes Prediction  \nSanmati Kumar Jain, Dr. Rajesh Keshavrao Deshmukh  \nDepartment of Computer Science & Engineering, Kalinga University, Naya Raipur, Chhattisgarh, India  \nAbstract – Because of its increasing prevalence and the implications that are associated with it, diabetes mellitus, which is a chronic metabolic disorder that is characterized by hyperglycemia, poses a significant threat to the health of people all over the world. Forecasting diabetes in a manner that is both accurate and timely is absolutely necessary for effective management and preventative approaches. Through the application of machine learning methods, this study comes up with a model that can accurately predict diabetes. The predictive model utilizes supervised machine learning techniques, specifically Decision Tree, Naïve Bayes, Artificial Neural Network, and Logistic Regression. These techniques are applied to provide accurate predictions. A number of performance criteria, like as accuracy, recall, precision, and F-score, have been utilized in order to carry out the comparison of different techniques.  \nKeywords: Supervised learning, Accuracy, Precision, Recall, Diabetes  \nI. INTRODUCTION  \nA chronic metabolic disease defined by persistently high blood glucose levels, diabetes mellitus has recently risen to the ranks of the world's most critical health issues. Factors including sedentary lifestyles, changes in food, and urbanization have contributed to the fast rise in the prevalence of diabetes. Worldwide, 451 million individuals are living with diabetes, and that number is expected to skyrocket in the next decades, according to the International Diabetes Federation (IDF) . Cardiovascular disease, neuropathy, nephropathy, and retinopathy are some of the long-term consequences of diabetes that can be lessened with early diagnosis and treatment. Although they are efficient, traditional diagnostic methods can be difficult and expensive for patients to afford or schedule. One game-changing strategy for better diabetes prediction and diagnosis in this setting is the use of ML algorithms.  \nMachine learning is a branch of AI that uses various algorithms and approaches to teach computers to learn from examples, so they can make judgments or predictions without human intervention. There is a lot of hope that using ML algorithms in healthcare might increase diagnostic accuracy, decrease costs, and pave the way for customized therapy, especially in the area of illness prediction. In order to develop predictive models that can identify individuals at risk of developing diabetes before clinical symptoms appear, the field of diabetes prediction through ML utilizes a variety of data sources, such as medical imaging, genetic information, and electronic health records (EHRs) .  \nDiabetes prediction has made use of many ML algorithms, each with its own set of benefits and drawbacks. Because of  \nits simplicity and interpretability, logistic regression—a basic approach in statistical modeling—is frequently utilized as a baseline for comparison. When dealing with non-linear correlations and interactions between features, decision trees and random forests—which employ hierarchical decisionmaking processes—tend to be preferred. For highdimensional data, strong frameworks for capturing complicated patterns are neural networks, support vector machines (SVMs), and deep learning models. A more accurate analysis of complex datasets, such medical pictures and time-series data, is now possible because to recent developments in deep learning, especially convolutional neural networks (CNNs) and recurrent neural networks (RNNs) .  \nThe capacity of the model to generalize to previously unknown data, the amount and quality of the data, and the technique chosen are all crucial to the success of ML algorithms in diabetes prediction. The success or failure of machine learning models is heavily dependent on the qual","cbCairZdS12c9MeQ","https://ap.wps.com/l/cbCairZdS12c9MeQ","pdf",200976,1,4,"English","en",105,"# Introduction\n## Motivation for ML-based diabetes prediction\n## ML algorithms used in diabetes prediction\n## Evaluation measures for predictive performance\n# Review of Literature\n## Reported findings and algorithm comparisons","[{\"question\":\"Which supervised machine learning algorithms are used for diabetes prediction in this study?\",\"answer\":\"The study uses Decision Tree, Naïve Bayes, Artificial Neural Network, and Logistic Regression as the supervised models for diabetes prediction.\"},{\"question\":\"How does the paper compare different prediction techniques?\",\"answer\":\"It compares models using accuracy, recall, precision, and F-score, along with ROC-AUC mentioned as part of model assessment.\"},{\"question\":\"Why is data preparation and feature selection important for ML diabetes prediction?\",\"answer\":\"Model success depends on data quality and preparation, including handling missing values, standardizing data, and selecting relevant features to enable reliable predictions.\"}]","Comparative Analysis of Supervised Machine Learning Algorithms for Diabetes Prediction | 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supervised machine learning algorithms are used for diabetes prediction in this study?","Question",{"text":74,"@type":75},"The study uses Decision Tree, Naïve Bayes, Artificial Neural Network, and Logistic Regression as the supervised models for diabetes prediction.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the paper compare different prediction techniques?",{"text":79,"@type":75},"It compares models using accuracy, recall, precision, and F-score, along with ROC-AUC mentioned as part of model assessment.",{"name":81,"@type":72,"acceptedAnswer":82},"Why is data preparation and feature selection important for ML diabetes prediction?",{"text":83,"@type":75},"Model success depends on data quality and preparation, including handling missing values, standardizing data, and selecting relevant features to enable reliable 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