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  Scopus ID: 21100926589

Machine Learning for Advanced Diabetes Prediction: Innovations in Health Technology and Diagnosis

Ramya Gidigam, Sri Nithya Gandham, Ankitha Vamala and Deepika Rani Namani

Abstract

Diabetes is a persistent medical condition which effect people of all age groups. It is caused by the body’s excessive glucose levels. The indicators of this elevated glucose level include high urinary frequency, increased appetite, and increased thirst. Diabetes shouldn’t be ignored because, if untreated, it can have major side effects on the body’s organs, including damage to the eyes, kidneys, heart, and other systems. If it is anticipated earlier, it can be managed. It’s crucial to identify diabetes earlier and lessen its symptoms. In this paper, we intend to create a model that predicts diabetes more precisely using a variety of machine learning (ML) classification techniques. Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Decision Tree (DT), and Naive Bayes are the algorithms that are examined to determine which model is most effective for predicting diabetes.

Published on: December 06, 2023
doi: 10.17756/nwj.2023-s4-078
Citation: Gidigam R, Gandham SN, Vamala A, Namani DR. 2023. Machine Learning for Advanced Diabetes Prediction: Innovations in Health Technology and Diagnosis. NanoWorld J 9(S4): S467-S469.

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