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This study evaluates whether explainable, supervised machine learning decision-support can detect clinical depression using publicly available Korean health data from the Korean National Health and Nutrition Examination Survey. Interpretable analyses combine model optimization, feature importance visualization, and calibration to support population- and individual-level understanding of nutrition-related risk markers.","TYPE Original Research PUBLISHED 09 May 2023  \nDOI 10. 3389/fnut.2023.1165854  \nOPEN ACCESS  \nEDITED BY  \nRoberta Zupo,  \nNational [Institute of Gastroenterology S. de](Institute of Gastroenterology S. de)[ ](Institute of Gastroenterology S. de)Bellis Research Hospital (IRCCS), Italy  \nREVIEWED BY  \nNguyen Quoc Khanh Le, Taipei Medical University, Taiwan Shahriar Faghani,  \nMayo Clinic, United States  \n*CORRESPONDENCE  \nSang-Ah Lee  \n [sangahlee@kangwon.ac.kr](sangahlee@kangwon.ac.kr)  \nRECEIVED 14 February 2023  \nACCEPTED 27 March 2023  \nPUBLISHED 09 May 2023  \nCITATION  \nHosseinzadeh Kasani P, Lee JE, Park C, Yun C-H, Jang J-W and Lee S-A (2023)  \nEvaluation of nutritional status and clinical depression classification using an explainable machine learning method.  \nFront. Nutr. 10:1165854 .  \ndoi: 10.3389/fnut.2023.1165854  \nCOPYRIGHT  \n© 2023 Hosseinzadeh Kasani, Lee, Park, Yun, Jang and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nEvaluation of nutritional status and clinical depression classification using an explainable machine learning method  \nPayam Hosseinzadeh Kasani1,2 , Jung Eun Lee2 , Chihyun Park2,3 , Cheol-Heui Yun4,5 , Jae-Won Jang1,6 and Sang-Ah Lee2,7*  \n1 Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea,  \n2 Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea, 3 Department of Computer Science and Engineering, Kangwon National University, Chuncheon, Republic of Korea, 4 Department of Agricultural Biotechnology, Seoul National University, Seoul, Republic of Korea, 5 Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea, 6 Department of Neurology, Kangwon National University School of Medicine, Chuncheon, Republic of Korea, 7 Department of Preventive Medicine, College of Medicine, Kangwon National University, Chuncheon, Republic of Korea  \nIntroduction: Depression is a prevalent disorder worldwide, with potentially severe implications. It contributes significantly to an increased risk of diseases associated with multiple risk factors. Early accurate diagnosis of depressive symptoms is a critical first step toward management, intervention, and prevention. Various nutritional and dietary compounds have been suggested to be involved in the onset, maintenance, and severity of depressive disorders. Despite the challenges to better understanding the association between nutritional risk factors and the occurrence of depression, assessing the interplay of these markers through supervised machine learning remains to be fully explored.  \nMethods: This study aimed to determine the ability of machine learningbased decision support methods to identify the presence of depression using publicly available health data from the Korean National Health and Nutrition Examination Survey. Two exploration techniques, namely, uniform manifold approximation and projection and Pearson correlation, were performed for explanatory analysis among datasets. A grid search optimization with crossvalidation was performed to fine-tune the models for classifying depression with the highest accuracy. Several performance measures, including accuracy, precision, recall, F1 score, confusion matrix, areas under the precision-recall and receiver operating characteristic curves, and calibration plot, were used to compare classifier performances. We further investigated the importance of the features provided: visualized interpretation using ELI5, partial dependence plots, and local interpreta","cbCaiv94QmOxO7cI","https://ap.wps.com/l/cbCaiv94QmOxO7cI","pdf",7438271,1,22,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion\n# Keywords","[{\"question\":\"What data source is used to evaluate depression classification?\",\"answer\":\"The study uses publicly available health data from the Korean National Health and Nutrition Examination Survey.\"},{\"question\":\"Which approaches are used for interpretability in the machine learning pipeline?\",\"answer\":\"The workflow includes explanatory analysis using techniques such as uniform manifold approximation and projection and correlation, plus feature interpretation via ELI5, partial dependence plots, and SHAP-style model-agnostic explanations.\"},{\"question\":\"How well do the best models perform in classifying clinical depression?\",\"answer\":\"The best model reports an accuracy around 86% with corresponding AUC values in the mid-80s, comparing results across original and quantile-based datasets and different classifiers such as XGBoost and random forest.\"}]","Evaluation of nutritional status and clinical depression classification using an explainable machine learning method | 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