Machine Learning for Diabetes and Blood Glucose Prediction

By: Barbosa

This book describes various ML techniques and DL algorithms used to apply in diabetes prediction.such as

NB, RF, SVM, DT, J48, Sequential Minimal Optimization (SMO), ANN (Artificial Neural Network), Multi-Layer

Perceptron (MLP), Recurrent Neural Network (RNN), CNN and LSTM. Although

deep research in diabetes mellitus has provided strong proficiency to classify data,

there is still much to be discovered by considering the patient's diet, medication,

activity, and biological, environmental, behavioral, and decision factors. Traditional

ML models are easy to interpret but achieve less predictive accuracy while ensemble

models provide higher accuracy. Existing studies proved that DL models performed

comparatively better on complex datasets than the ML models.

• Format: Paperback
• Language: English
• ISBN: B0HGTGL7S2
• ISBN13: 9798240880537
• Release Date: August 2026
• Publisher: Barbosa
• Length: 134 Pages
• Weight: 0.42 lbs.
• Dimensions: 0.3" x 6.0" x 9.0"