Detection and classification of anxiety in university students through the application of machine learning
Shaurya Bhatnagar, Jyoti Agarwal, Ojasvi Rajeev Sharma
Abstract
Mental Health has recently transformed into a domain that caused interest in almost every field and has garnered attention in recent years, millions of people suffer due to mental illnesses that are often left unaccounted for, a vast majority of these people are youngsters, a large chunk being students belonging to various universities. This research's aim was to pinpoint the extent of anxiety, in conjunction, its effects, as noticed in Indian university students. The dataset was gathered using a questionnaire, that was matching with Likert scale measurement criteria, which consisted of university engineering students. This questionnaire was distributed amongst 127 engineering students, as a result, the level of anxiety was quantified, and its causes and effects were identified, a series of statistical reliability and validity tests were performed on the dataset. Machine learning algorithms are applied in the end to classify the anxiety level based on the effects of anxiety after being trained on pre-existing data points. It was found that the Cronbach's alpha value for the entire dataset was 0.723 and Pearson's correlation coefficient was 0.823, the accuracy for the naïve bayes, decision tree, random forest and support vector machine algorithms were 71.05%, 71.05%, 78.9% and 75.5% respectively.