Publication: LOGISTIC CLASSIFICATION IN DIAGNOSING ACUTE FUNCTIONAL IMPAIRMENT IN MEN WITH MAJOR DEPRESSION
Date
2023
Authors
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Publisher
International Medical University
Abstract
Although men are diagnosed with depression half as frequently as women and are
unlikely to seek suicide, men are in fact more prone to death by suicide up to three to
four times as often. Ultimately, it is highlighted that rarely do men pursue help yet are
engaging in detrimental behaviours all the same at a greater prevalence than that of
women. To improve mental health among men, a crucial measure to take is to raise
their inclination in seeking aid for depression and associated functional impairment.
Consequently, two research objectives are determined for the present study. Firstly, to
ascertain the direction and strength of association between sociodemographic
characteristics and Major Depressive Episode with Severe Impairment (MDESI) in
men by utilising a nomogram. Secondly, to develop a logistic regression predictive
model to classify men diagnosed with MDESI into categories with and without severe
functional impairment. Data on adult men aged 18 years and above who have
participated in the National Survey on Drug Use and Health (NSDUH), 2020 to 2021,
are pooled and analysed. The nomogram has revealed that Native American men are
at highest risk of experiencing MDESI compared to men of other ethnicities.
Additionally, for men, being at an age between 50 to 64 years, having a family income
of less than 20,000$ (US), being gay, strongly disagreeing with the importance of
friends sharing religious beliefs, strongly agreeing with the importance of personal
religious beliefs, agreeing with religious beliefs influencing personal decisions, and
living at a non-metro area further increase the risk of experiencing MDESI. Using the
training data set, the logistic regression predictive model has produced AUC = 0.733,
accuracy = 0.638, recall = 0.638, and precision = 0.697 . Using the test data set, the
scores have slightly increased for all measures (AUC = 0.746, accuracy = 0.678, recall
= 0.678, precision = 0.729). Study results have, however, indicated that the current
logistic model, when utilised as a classifier, is presently performing inadequately.
Further work is required in order to enhance the overall model to be at a more adequate
state.
Keywords
men’s mental health, major depression, severe impairment, National Survey on Drug
Use and Health, machine learning, logistic regression classifier
Description
Keywords
Depression, Men, Suicide, Mental Health, Machine Learning