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 licensed under Creative Commons License and full text is openly accessible in CUD Digital Repository. The version of the scholarly record of this article 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 |