Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea

Abstract

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.

Predicting South Korea adolescents vulnerable to depressive disorder using Bayesian nomogram: A community-based cross-sectional study

“…Where c is the target class of the nomogram. However, if it is a class other than c , and P(|X) represents the probability that object X does not belong to class c[ 9 ], then the odds ratio (OR) for these two probabilities can be calculated as:…”

“…Recently, the naïve Bayesian nomogram has been used as a method for predicting groups at high risk of developing diseases[ 8 , 9 ]. One of the advantages of this method is that it presents the risk probability according to multiple risk factors of a disease visually so that clinicians can easily understand the results[ 10 ].…”

Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model

“…In addition, many researchers use machine learning models to predict depression in patients with chronic diseases such as hypertension, diabetes, stroke, cancer, etc [ 18 – 23 ]. Byeon et al [ 24 ] used stacking ensemble machine technology to analyze epidemiological survey data on depression among elderly women living alone in South Korea, explored the major risk factors for depression, and developed a nomogram to help primary care physicians easily interpret high-risk populations for depression in primary care settings. Three different ensemble learning classifiers, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were used to analyze the mental health data of 15,173 older adults and to diagnose and identify mental health disorders such as depression in older adults, and the ensemble learning classifiers were evaluated, and it was found that all three classifiers achieved good prediction results, with the LightGBM algorithm having a higher accuracy rate than the Random Forest algorithm and the XGBoost algorithm [ 25 ].…”

Prediction of adolescent suicidal ideation after the COVID-19 pandemic: A nationwide survey of a representative sample of Korea

“…The logistic regression model is a stochastic model widely used to predict the likelihood of an event by linearly combining independent variables when the dependent variable is binomial. Although logistic regression analysis has the advantage of being able to identify the influence of individual variables on a dependent variable, it has a limitation in understanding the interaction between various explanatory variables used for the predictive model because it assumes independence that the effect of an explanatory variable does not depend on the level of other explanatory variables ( 14 ). As a way to overcome these limitations of regression analysis, many recent studies ( 15 , 16 ) are widely using boosting-based machine learning models such as XGBoost (extreme gradient boosting).…”