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Document Type

Article

Keywords

Machine Learning, SMOTE for Imbalanced Data, Class Imbalance, Ensemble Learning, Mobile Health (mHealth), Healthcare Analytics, Diabetes Prediction, Machine Learning in Healthcare

Abstract

Diabetes is a widespread disease worldwide that does not differentiate between children and adults. It also affects the elderly and pregnant women. However, early detection of the disease facilitates its control to avoid the effects resulting from delayed diagnosis. With the emergence of artificial intelligence represented by machine learning techniques and its use in most sectors, accordingly, the adoption of machine learning techniques to help in disease prediction has become a necessity. This study proposes a machine learning algorithm-based approach for diabetes prediction. This study uses three datasets, two of which are private and the other includes the Pima Indians dataset. Six machine-learning models have been used and evaluated in this study including Gaussian Naive Bayes model. Bernoulli Naive Bayes Model, Bagging Regressor Model, Neural Network Architecture, Multilayer perceptron, and SVR. To address the imbalance in classes of the private datasets, the SMOTE technique has been utilized. To analyze the state of the arts, a systematic literature review was conducted. The results showed that the Bagging Regressor algorithm is the best among the used algorithms in terms of the accuracy of the derived results. It achieved an accuracy of 99.79 with SMOTE included and 97.95 without SMOTE. A smart mobile application was developed based on the proposed approach that facilitates clinicians to predict diabetes. This study strengthens the theoretical foundations of machine learning in healthcare by presenting a robust and empirically validated approach for early detection and prediction of diabetes. The findings not only advance academic knowledge but also provide practical guidance for developing AI-based diagnostic tools in clinical settings.

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