https://ajouronline.com/index.php/AJCIS Asian Online Journals en-US Asian Journal of Computer and Information Systems 2321-5658 <ul> <li class="show">Papers must be submitted on the understanding that they have not been published elsewhere (except in the form of an abstract or as part of a published lecture, review, or thesis) and are not currently under consideration by another journal published by any other publisher.</li> <li class="show">It is also the authors responsibility to ensure that the articles emanating from a particular source are submitted with the necessary approval.</li> <li class="show">The authors warrant that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required.</li> <li class="show">The authors ensure that all the references carefully and they are accurate in the text as well as in the list of references (and vice versa).</li> <li class="show">Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a&nbsp;<a href="https://creativecommons.org/licenses/by-nc/4.0/" target="_new">Attribution-NonCommercial 4.0 International</a> that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</li> <li class="show">Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li> <li class="show">Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See&nbsp;<a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</li> <li class="show">The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.</li> </ul> https://ajouronline.com/index.php/AJCIS/article/view/6882 <p>Student’s mental health problems have been explored previously in higher education literature in various contexts including empirical work involving quantitative and qualitative methods. Nevertheless, comparatively few research could be found, aiming for computational methods that learn information directly from data without relying on set parameters for a predetermined equation as an analytical method. This study aims to investigate the performance of Machine learning (ML) models used in higher education. ML models considered are Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Stochastic Gradient Descent, Decision Tree, Random Forest, XGBoost (Extreme Gradient Boosting Decision Tree), and NGBoost (Natural) algorithm. Considering the factors of mental health illness among students, we follow three phases of data processing: segmentation, feature extraction, and classification. We evaluate these ML models against classification performance metrics such as accuracy, precision, recall, F1 score, and predicted run time. The empirical analysis includes two contributions: 1. It examines the performance of various ML models on a survey-based educational dataset, inferring a significant classification performance by a tree-based XGBoost algorithm; 2. It explores the feature importance [variables] from the datasets to infer the significant importance of social support, learning environment, and childhood adversities on a student’s mental health illness.</p> Prathamesh Muzumdar Ganga Prasad Basyal Piyush Vyas Copyright (c) 2022 Prathamesh Muzumdar, Ganga Prasad Basyal, Piyush Vyas https://creativecommons.org/licenses/by-nc/4.0 2022-02-27 2022-02-27 10 1 10.24203/ajcis.v10i1.6882