Prediction of diabetes empowered with fused machine learning

dc.contributor.author Ahmed, Usama
dc.contributor.author Issa, Ghassan F.
dc.contributor.author Khan, Muhammad Adnan
dc.contributor.author Aftab, Shabib
dc.contributor.author Khan, Muhammad Farhan
dc.contributor.author Said, Raed A. T.
dc.contributor.author Ghazal, Taher M.
dc.contributor.author Ahmad, Munir
dc.date.accessioned 2022-02-03T13:50:19Z
dc.date.available 2022-02-03T13:50:19Z
dc.date.issued 2022
dc.description This article is not available at CUD collection. The version of scholarly record of this article paper is published in IEEE Access (2022), available online at: https://doi.org/10.1109/ACCESS.2022.3142097 en_US
dc.description.abstract In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. These models analyze the dataset to determine whether a diabetes diagnosis is positive or negative. The dataset used in this research is divided into training data and testing data with a ratio of 70:30 respectively. The output of these models becomes the input membership function for the fuzzy model, whereas the fuzzy logic finally determines whether a diabetes diagnosis is positive or negative. A cloud storage system stores the fused models for future use. Based on the patient’s real-time medical record, the fused model predicts whether the patient is diabetic or not. The proposed fused ML model has a prediction accuracy of 94.87, which is higher than the previously published methods. Author en_US
dc.identifier.citation Ahmed, U., Issa, G. F., Khan, M. A., Aftab, S., Khan, M. F., Said, R. A. T., . . . Ahmad, M. (2022). Prediction of diabetes empowered with fused machine learning. IEEE Access, 10, 8529-8538. https://doi.org/10.1109/ACCESS.2022.3142097 en_US
dc.identifier.issn 21693536
dc.identifier.uri https://doi.org/10.1109/ACCESS.2022.3142097
dc.identifier.uri http://hdl.handle.net/20.500.12519/503
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation Authors Affiliations : Ahmed, U., Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan and Department of Computer Science, Virtual University of Pakistan, Lahore, 54000, Pakistan.; Issa, G.F., School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAE.; Aftab, S., Department of Computer Science, Virtual University of Pakistan, Lahore, 54000, Pakistan and School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan.; Khan, M.F., Department of Forensic Sciences, University of Health Sciences, Lahore, 54000, Pakistan.; Said, R.A.T., Faculty of Management, Canadian University Dubai, 117781, United Arab Emirates.; Ghazal, T.M., School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAE and Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.; Ahmad, M., School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan.; Khan, M.A., Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13557, South Korea.
dc.relation.ispartofseries IEEE Access; Volume 10
dc.rights Creative Commons Attribution 4.0 International (CC BY 4.0) License
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Diabetes en_US
dc.subject Diabetic prediction en_US
dc.subject Diabetic symptoms en_US
dc.subject Disease prediction en_US
dc.subject Diseases en_US
dc.subject Fused machine learning model en_US
dc.subject Fuzzy system en_US
dc.subject Machine learning en_US
dc.subject Machine learning algorithms en_US
dc.subject Mathematical models en_US
dc.subject Prediction algorithms en_US
dc.subject Support vector machines en_US
dc.title Prediction of diabetes empowered with fused machine learning en_US
dc.type Article en_US
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