Clinical Decision Support System for Diabetes Classification with an Optimized CNN using PSO

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dc.contributor.authorKhan, F.A.
dc.contributor.authorJamil, A.
dc.contributor.authorHameed, A.A.
dc.contributor.authorMoetesum, M.
dc.date.accessioned2024-05-19T14:33:43Z
dc.date.available2024-05-19T14:33:43Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.descriptionCentral Michigan University (CMU);IEEEen_US
dc.description2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023 -- 16 September 2023 through 17 September 2023 -- -- 194014en_US
dc.description.abstractDiabetes, a pervasive chronic metabolic disorder, affects a substantial portion of the global population. The timely and precise diagnosis of diabetes is pivotal for effective management and improved patient outcomes. In this study, we introduce an innovative approach aimed at augmenting the classification performance of Convolutional Neural Networks (CNNs) in diabetes diagnosis. Utilizing the Particle Swarm Optimization (PSO) algorithm, we fine-tune the CNN model to enhance the accuracy and efficiency of diabetes classification. Our comprehensive experiments, conducted on the Pima Indians Diabetes dataset, substantiate the effectiveness of our optimized CNN. These findings underscore the potential of integrating CNN and PSO optimization methodologies to significantly boost the accuracy of diabetes diagnosis, thereby facilitating more accurate assessments and tailored treatment strategies for patients. We evaluated the proposed model using standard metrics such as precision, recall, F1-score, and overall accuracy, with results demonstrating that the PSO-based optimized CNN model outperforms the custom CNN, achieving the highest precision, recall, F1-score, and overall accuracy. © 2023 IEEE.en_US
dc.identifier.doi10.1109/AIBThings58340.2023.10292468
dc.identifier.isbn9798350322347
dc.identifier.scopus2-s2.0-85178521925en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/AIBThings58340.2023.10292468
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4319
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectDecision Support Systemen_US
dc.subjectDiabetes Classificationen_US
dc.subjectParticle Swarm Optimizationen_US
dc.titleClinical Decision Support System for Diabetes Classification with an Optimized CNN using PSOen_US
dc.typeConference Objecten_US

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