Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram

Using Samples Representing South Korea

Bibliographic Data

ID15526767
AuthorsHaewon Byeon (0000-0002-3363-390X, Inje University, corresponding author)
Year2022
Volume12
Publication date2022-01-07
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2021.773290
OpenAlexW4206326187
LanguageEN
Citations received2
References cited7

This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South Koreans. To achieve the study objective, this study explored the main risk factors of depressive disorders using the stacking ensemble machine technique. Moreover, this study developed a nomogram that could help primary physicians easily interpret high-risk groups of depressive disorders in primary care settings based on the major predictors derived from machine learning. This study analyzed 582 female older adults (≥60 years old) living alone. The depressive disorder, a target variable, was measured using the Korean version of Patient Health Questionnaire-9. This study developed five single predictive models (GBM, Random Forest, Adaboost, SVM, XGBoost) and six stacking ensemble models (GBM + Bayesian regression, RandomForest + Bayesian regression, Adaboost + Bayesian regression, SVM + Bayesian regression, XGBoost + Bayesian regression, GBM + RandomForest + Adaboost + SVM + XGBoost + Bayesian regression) to predict depressive disorders. The naive Bayesian nomogram confirmed that stress perception, subjective health, n-6 fatty acid, n-3 fatty acid, mean hours of sitting per day, and mean daily sleep hours were six major variables related to the depressive disorders of female older adults living alone. Based on the results of this study, it is required to evaluate the multiple risk factors for depression including various measurable factors such as social support

AdaBoost · Depression (economics · Logistic regression · Machine learning · Major depressive disorder · Mood · Naive Bayes classifier · Nomogram · Psychiatry · Support vector machine · Computer Science · Health and Wellbeing Research · Health disparities and outcomes · Medicine · Nutrition, Health and Food Behavior · Artificial Intelligence · Internal Medicine

Tools

Open DOIOpen Access