Comparing the Balanced Accuracy of Deep Neural Network and Machine Learning for Predicting the Depressive Disorder of Multicultural Youth

Abstract

Multicultural youth are more likely to experience negative emotions (e.g. depressive symptoms) due to social prejudice and discrimination. Nevertheless, previous studies that analyzed the emotional aspects of multicultural youth mainly compared the characteristics of multicultural youth and those of the other youth or identified individual risk factors using a regression model. This study developed models to predict the depressive disorders of multicultural youth based on the Quick Unbiased Efficient Statistical Tree (QUEST), Classification And Regression Trees (CART), gradient boosting machine (G-B-M), random forest, and deep neural network (deep-NN) using epidemiological data representing multicultural youth and compared the prediction performance (PRED PER) of the developed models. Our study analyzed 19,431 youths (9,835 males and 9,596 females) aged between 19 and 24 years old. We developed models for predicting the self-awareness of health of youths by using QUEST, CART, G-B-M, random forest, and deep-NN and compared the balanced accuracy of them to evaluate their PRED PER. Among 19,431 subjects, 42.9% (5,838 people) experienced a depressive disorder in the past year. The results of our study confirmed that deep-NN had the best PRED PER with a specificity of 0.85, a sensitivity of 0.71, and a balanced accuracy of 0.78. It will be necessary to develop a model with optimal PRED PER by tuning hyperparameters (e.g., number of hidden layers, number of iterations, and activation function, number of hidden nodes) of deep-NN.