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View all- Chakraborty SKairi ADutta Roy NMaity SPandey SDe I(2023)Market-Based Stock Allocation Using a Hybrid Regression ModelSN Computer Science10.1007/s42979-023-01883-14:4Online publication date: 1-Jun-2023
Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: : A Case Study of District Bandipora
Authors: 
Salliah Shafi Bhat, Venkatesan Selvam, Gufran Ahmad Ansari, Mohd Dilshad Ansari, Md Habibur Rahman Academic Editor: Mamoon RashidAuthors Info & Claims
Salliah Shafi Bhat, Venkatesan Selvam, Gufran Ahmad Ansari, Mohd Dilshad Ansari, Md Habibur Rahman Academic Editor: Mamoon RashidAuthors Info & ClaimsAbstract
Diabetes is one of the biggest health problems that affect millions of people across the world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage, blindness, and other illnesses. Researchers are motivated to create a Machine Learning methodology that can predict diabetes in the future. Exploiting Machine Learning Algorithms (MLA) is essential if healthcare professionals are able to identify diseases more effectively. In order to improve the medical diagnosis of diabetes this research explored and contrasts various MLA that can identify diabetes risk early. The research includes the analysis on real datasets such as a clinical dataset gathered from a doctor in the Indian district of Bandipora in the years April 2021–Feb2022. MLA are currently important in the healthcare sector due to their prediction abilities. Researchers are using MLA to improve disease prediction and reduce cost. In this Paper author developed a methodology using Machine Learning Algorithms for Diabetes Disease Risk Prediction in North Kashmir. Six MLA have been successfully used in the experimental study such as Random Forest (RF), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boost (GB), Decision Tree (DT), and Logistic Regression (LR). RF is the most accurate classifier with the uppermost accuracy rate of 98 percent followed by MLP (90.99%), SVM (92%), GBC (97%), DT (96%), and LR (69%), respectively, with the balanced data set. Lastly, this study enables us to effectively identify the prevalence and prediction of diabetes.
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Copyright © 2022 Salliah Shafi Bhat et al.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Published: 01 January 2022
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View all- Chakraborty SKairi ADutta Roy NMaity SPandey SDe I(2023)Market-Based Stock Allocation Using a Hybrid Regression ModelSN Computer Science10.1007/s42979-023-01883-14:4Online publication date: 1-Jun-2023