Prediction of diabetes empowered with fused machine learning

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dc.contributor.authorAhmed, Usama
dc.contributor.authorIssa, Ghassan F.
dc.contributor.authorKhan, Muhammad Adnan
dc.contributor.authorAftab, Shabib
dc.contributor.authorKhan, Muhammad Farhan
dc.contributor.authorSaid, Raed A. T.
dc.contributor.authorGhazal, Taher M.
dc.contributor.authorAhmad, Munir
dc.date.accessioned2022-02-03T13:50:19Z
dc.date.available2022-02-03T13:50:19Z
dc.date.issued2022
dc.description.abstractIn 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. Authoren_US
dc.identifier.citationAhmed, 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.2022.3142097en_US
dc.identifier.issn21693536
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2022.3142097
dc.identifier.urihttp://hdl.handle.net/20.500.12519/503
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relationAuthors 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.ispartofseriesIEEE Access; Volume 10
dc.rightsCreative Commons Attribution 4.0 International (CC BY 4.0) License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDiabetesen_US
dc.subjectDiabetic predictionen_US
dc.subjectDiabetic symptomsen_US
dc.subjectDisease predictionen_US
dc.subjectDiseasesen_US
dc.subjectFused machine learning modelen_US
dc.subjectFuzzy systemen_US
dc.subjectMachine learningen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectMathematical modelsen_US
dc.subjectPrediction algorithmsen_US
dc.subjectSupport vector machinesen_US
dc.titlePrediction of diabetes empowered with fused machine learningen_US
dc.typeArticleen_US

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