Mind Matters: Exploring the Intersection of Psychological Factors and Cognitive Abilities of University Students by Using ANN Model

Mohsin Khan; Syed Khalid Perwez; Rahul Paul Gaddam; Rabuni Aiswarya; Mohammed Abrar Basha; Abhradeep Malas; Shafiul Haque; Faraz Ahmad

doi:10.2147/NDT.S436975

Mind Matters: Exploring the Intersection of Psychological Factors and Cognitive Abilities of University Students by Using ANN Model

Neuropsychiatr Dis Treat. 2024 Jan 22:20:137-148. doi: 10.2147/NDT.S436975. eCollection 2024.

Authors

Affiliations

¹ Department of Commerce, School of Social Science and Languages, Vellore Institute of Technology, Vellore, India.
² VIT Business School, Vellore Institute of Technology, Vellore, India.
³ School of Life Sciences, B.S Abdur Rahman Crescent Institute of Science & Technology, Chennai, India.
⁴ Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, India.
⁵ Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia.
⁶ Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon.
⁷ Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates.

^# Contributed equally.

Abstract

Purpose: While previous studies have suggested close association of psychological variables of students withtheir higher-order cognitive abilities, such studies have largely been lacking for third world countries like India, with their unique socio-economic-cultural set of challenges. We aimed to investigate the relationship between psychological variables (depression, anxiety and stress) and cognitive functions among Indian students, and to predict cognitive performance as a function of these variables.

Patients and methods: Four hundred and thirteen university students were systematically selected using purposive sampling. Widely used and validated offline questionnaires were used to assess their psychological and cognitive statuses. Correlational analyses were conducted to examine the associations between these variables. An Artificial Neural Network (ANN) model was applied to predict cognitive levels based on the scores of psychological variables.

Results: Correlational analyses revealed negative correlations between emotional distress and cognitive functioning. Principal Component Analysis (PCA) reduced the dimensionality of the input data, effectively capturing the variance with fewer features. The feature weight analysis indicated a balanced contribution of each mental health symptom, with particular emphasis on one of the symptoms. The ANN model demonstrated moderate predictive performance, explaining a portion of the variance in cognitive levels based on the psychological variables.

Conclusion: The study confirms significant associations between emotional statuses of university students with their cognitive abilities. Specifically, we provide evidence for the first time that in Indian students, self-reported higher levels of stress, anxiety, and depression are linked to lower performance in cognitive tests. The application of PCA and feature weight analysis provided deeper insights into the structure of the predictive model. Notably, use of the ANN model provided insights into predicting these cognitive domains as a function of the emotional attributes. Our results emphasize the importance of addressing mental health concerns and implementing interventions for the enhancement of cognitive functions in university students.

Keywords: DASS; Indian; MoCA; Montreal Cognitive Assessment; artificial neural network; college students; depression anxiety stress score; developing economies; feature reduction; feature weights; predictive performance.