User-cloud-based ensemble framework for type-2 diabetes prediction with diet plan suggestion

•Implementation of user-cloud environment for real-time applications Usually, a cloud application is deployed in the hospital environment, and a user application is deployed in the user's environment.•Train bagging ensemble classifiers with DTC, SVM, and ANN features of the PID dataset in a clo...

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Bibliographic Details
Published in:	e-Prime Vol. 7; p. 100423
Main Authors:	Prabhakar, G, Chintala, Vaishnavi Reddy, Reddy, Trishala, Ruchitha, T
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01-03-2024 Elsevier
Subjects:	Artificial neural networks Bagging ensemble classifier Cloud-based health analytics Decision tree classifier Diabetes detection Ensemble machine learning Health status prediction Pima Indian diabetes Support vector machine Type-2 diabetes prediction Cloud-based health analytics Artificial neural networks Ensemble machine learning Decision tree classifier Support vector machine Health status prediction Pima Indian diabetes Type-2 diabetes prediction Bagging ensemble classifier Diabetes detection
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Summary:	•Implementation of user-cloud environment for real-time applications Usually, a cloud application is deployed in the hospital environment, and a user application is deployed in the user's environment.•Train bagging ensemble classifiers with DTC, SVM, and ANN features of the PID dataset in a cloud environment.•They are testing the bagging ensemble classifier for type-2 diabetes prediction and diet plan suggestions in the user environment. Currently, many individuals are experiencing diabetes, which is attributed to work-related stress and unhealthy lifestyles. Often, people are only aware of their health status once symptoms manifest or they undergo a medical examination to obtain a diagnosis. However, the conventional machine learning methods failed to classify diabetes in the initial stage. Therefore, this work implements the user-cloud-based ensemble framework for type-2 diabetes prediction (DP-UCE). Initially, the Pima Indian Diabetes (PID) dataset is considered. Here, the cloud application trains the PID dataset using an ensemble model. Here, the dataset preprocessing is performed to remove missing values for unknown symbols. Then, three individual models, such as a decision tree classifier (DTC), support vector machine (SVM), and artificial neural network (ANN), were trained with the preprocessed dataset. Then, the bagging ensemble classifier is trained with the DTC, ANN, and SVM features. In addition, the user application is initialized, and type-2 diabetes status is predicted from the uploaded test data of the user using a bagging ensemble classifier. Moreover, based on prediction results, the user is also given a diet plan. The simulations conducted on the PID dataset show that the ensemble classifier resulted in 87.41 % accuracy, which is superior to state-of-the-art methods.
ISSN:	2772-6711 2772-6711
DOI:	10.1016/j.prime.2024.100423