eprintid: 17040
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/70/40
datestamp: 2023-12-19 03:23:30
lastmod: 2023-12-19 03:23:30
status_changed: 2023-12-19 03:07:21
type: article
metadata_visibility: show
creators_name: Ho, C.S.
creators_name: Chan, Y.L.
creators_name: Tan, T.W.
creators_name: Tay, G.W.
creators_name: Tang, T.B.
title: Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data
ispublished: pub
keywords: adult; algorithm; Article; controlled study; demography; diagnostic accuracy; diagnostic test accuracy study; female; functional near-infrared spectroscopy; Hamilton Depression Rating Scale; human; machine learning; major clinical study; major depression; male; nested cross validation; questionnaire; random forest; receiver operating characteristic; sensitivity and specificity; validation process; algorithm; machine learning; near infrared spectroscopy; procedures, Adult; Algorithms; Depressive Disorder, Major; Humans; Machine Learning; ROC Curve; Spectroscopy, Near-Infrared
note: cited By 1
abstract: Background: Given that major depressive disorder (MDD) is both biologically and clinically heterogeneous, a diagnostic system integrating neurobiological markers and clinical characteristics would allow for better diagnostic accuracy and, consequently, treatment efficacy. Objective: Our study aimed to evaluate the discriminative and predictive ability of unimodal, bimodal, and multimodal approaches in a total of seven machine learning (ML) models�clinical, demographic, functional near-infrared spectroscopy (fNIRS), combinations of two unimodal models, as well as a combination of all three�for MDD. Methods: We recruited 65 adults with MDD and 68 matched healthy controls, who provided both sociodemographic and clinical information, and completed the HAM-D questionnaire. They were also subject to fNIRS measurement when participating in the verbal fluency task. Using the nested cross validation procedure, the classification performance of each ML model was evaluated based on the area under the receiver operating characteristic curve (ROC), balanced accuracy, sensitivity, and specificity. Results: The multimodal ML model was able to distinguish between depressed patients and healthy controls with the highest balanced accuracy of 87.98 ± 8.84 (AUC = 0.92; 95 CI (0.84�0.99) when compared with the uni- and bi-modal models. Conclusions: Our multimodal ML model demonstrated the highest diagnostic accuracy for MDD. This reinforces the biological and clinical heterogeneity of MDD and highlights the potential of this model to improve MDD diagnosis rates. Furthermore, this model is cost-effective and clinically applicable enough to be established as a robust diagnostic system for MDD based on patients� biosignatures. © 2022 Elsevier Ltd
date: 2022
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122960799&doi=10.1016%2fj.jpsychires.2022.01.026&partnerID=40&md5=123f2b7c1ae89fa6a7092f0c8ef150f9
id_number: 10.1016/j.jpsychires.2022.01.026
full_text_status: none
publication: Journal of Psychiatric Research
volume: 147
pagerange: 194-202
refereed: TRUE
issn: 00223956
citation:   Ho, C.S. and Chan, Y.L. and Tan, T.W. and Tay, G.W. and Tang, T.B.  (2022) Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data.  Journal of Psychiatric Research, 147.  pp. 194-202.  ISSN 00223956