Prediction of depression status in college students using a Naive Bayes classifier based machine learning model

TL;DR

This paper develops a Naive Bayes machine learning model to predict depression levels in college students, achieving 78.03% accuracy and demonstrating high sensitivity and specificity for early mental health intervention.

Contribution

It introduces a novel application of Naive Bayes classifier for depression prediction in students with improved accuracy and early detection capabilities.

Findings

Accuracy of 78.03% in predicting depression levels

High sensitivity in detecting positive depression cases

Significant specificity in correctly classifying negative cases

Abstract

This study presents a machine learning model based on the Naive Bayes classifier for predicting the level of depression in university students, the objective was to improve prediction accuracy using a machine learning model involving 70% training data and 30% validation data based on the Naive Bayes classifier, the collected data includes factors associated with depression from 519 university students, the results showed an accuracy of 78.03%, high sensitivity in detecting positive cases of depression, especially at moderate and severe levels, and significant specificity in correctly classifying negative cases, these findings highlight the effectiveness of the model in early detection and treatment of depression, benefiting vulnerable sectors and contributing to the improvement of mental health in the student population.