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dc.contributor.author Yasodya, GDS
dc.contributor.author Ganegoda, GU
dc.contributor.editor Piyatilake, ITS
dc.contributor.editor Thalagala, PD
dc.contributor.editor Ganegoda, GU
dc.contributor.editor Thanuja, ALARR
dc.contributor.editor Dharmarathna, P
dc.date.accessioned 2024-02-06T06:02:05Z
dc.date.available 2024-02-06T06:02:05Z
dc.date.issued 2023-12-07
dc.identifier.uri http://dl.lib.uom.lk/handle/123/22183
dc.description.abstract Alzheimer's disease is the most prevalent form of dementia with no established cure. Extensive research aims to comprehend its underlying mechanisms. Genetic insights are sought through gene expression data analysis, leveraging computational and statistical techniques to identify risk-associated genes. This study focuses on accurate AD detection using blood gene expression data. Four feature classification methods—TFrelated genes, Hub genes, CFG, and VAE are employed to identify crucial AD-related genes. Five classification approaches—RF, SVM, LR, L1-LR, and DNN—are used, evaluated by AUC. The VAE + LR model yields the highest AUC (0.76). The study identifies 100 influential AD-associated genes where data is sourced from Alzheimer's Disease Neuroimaging Initiative (ADNI). Findings hold promise for advancing early diagnosis and treatment, enhancing AD patients' quality of life. en_US
dc.language.iso en en_US
dc.publisher Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.subject Blood gene expression en_US
dc.subject Machine learning en_US
dc.subject Alzheimer’s disease en_US
dc.title Alzheimer’s disease detection using blood gene expression data en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty IT en_US
dc.identifier.department Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.identifier.year 2023 en_US
dc.identifier.conference 8th International Conference in Information Technology Research 2023 en_US
dc.identifier.place Moratuwa, Sri Lanka en_US
dc.identifier.pgnos pp. 1-6 en_US
dc.identifier.proceeding Proceedings of the 8th International Conference in Information Technology Research 2023 en_US
dc.identifier.email sudam.18@itfac.mrt.ac.lk en_US
dc.identifier.email upekshag@uom.lk en_US


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  • ICITR - 2023 [47]
    International Conference on Information Technology Research (ICITR)

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