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Diabetes Prediction Model Using Stochastic Gradient Descent Logistic Regression Approach
Abstract
Diabetes is a chronic disorder caused by either inadequate insulin production by the pancreas or inadequate insulin absorption by the body. Many machine learning approaches handle a wide range of chronic conditions and keep track of patient health data. The analysis of medical data from various angles and the creation of knowledge from it can be accomplished using a variety of machine learning techniques. Creating new features by combining two or more features can provide more insights for health-related data. It aids in revealing a data set's hidden relationships. This work implements LR, RFECV-LR, and RFECV-SGDLR for comparison purposes and comes with the best suitable classification model. Further, this work suggests an IoT-based diabetes model that can also record information about their location, body temperature, and blood glucose levels and can help patients live healthier lifestyles by tracking their activities and diets.
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