%0 Conference Paper
%A Lee, L.H.
%A Ho, C.S.H.
%A Tay, G.W.N.
%A Lu, C.-K.
%A Tang, T.B.
%D 2023
%F scholars:19027
%K Machine learning; Neuroimaging; RNA, Biological mechanisms; Clinical relevance- the discovery of miRNA candidate in this study can potentially narrow down the collection of blood miRNA sample for the treatment response prediction; Diagnosis planning; Learning studies; Machine-learning; Psychiatric disorders; Response levels; Treatment planning; Treatment response, Nearest neighbor search
%P 1134-1138
%R 10.1109/TENCON58879.2023.10322325
%T Modelling Major Depressive Disorder Antidepressant Treatment Response: A miRNA-based Machine Learning Study
%U https://khub.utp.edu.my/scholars/19027/
%X Major depressive disorder (MDD) is a psychiatric disorder but currently defined by symptoms rather than biological mechanism. This in turn sets a huge barrier to effective diagnosis and treatment planning. Investigations were done through neuropathogenesis and neuroimaging analysis as an effort to identify discriminative biomarkers for MDD while understanding the biological dependencies. The literature suggested that microRNA or miRNA transcripts are more likely to deliver substantial predictive power in diagnosis and antidepressant treatment response (ATR) prediction. Yet, there presents discrepancy in unique markers, and such discrepancy might be due to the small sample size over some of the reported studies. This study utilized miRNA as a predictor to model MDD ATR using k-nearest neighbour (kNN). The shortlisted miRNA through feature selection techniques scored 71.20, 68.13, 72.13, and 84.07 for three response levels in accuracy, sensitivity, specificity, and precision, respectively. Synthetic Minority Oversampling TEchnique (SMOTE) was then applied to the shortlisted miRNA and three response levels reported at least 98 in each of the mentioned performance metric. © 2023 IEEE.
%Z cited By 0; Conference of 38th IEEE Region 10 Conference, TENCON 2023 ; Conference Date: 31 October 2023 Through 3 November 2023; Conference Code:194660