<ul><li>Diabetes poses a significant health challenge globally, leading to severe complications such as kidney disease, vision loss, and heart issues.</li><li>Machine learning (ML) is being utilized in healthcare to predict diseases efficiently, allowing for early intervention and patient support.</li><li>A study introduces a novel diabetes prediction framework that combines traditional ML techniques like Logistic Regression, SVM, Naive Bayes, and Random Forest with advanced ensemble methods such as AdaBoost, Gradient Boosting, Extra Trees, and XGBoost.</li><li>The study's innovative DNet model, which merges Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers, achieves high accuracy of 99.79% and an AUC-ROC of 99.98%, demonstrating its potential for superior diabetes prediction.</li></ul>

Predicting Diabetes Using Machine Learning: A Comparative Study of Classifiers

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