Comparative Analysis of Machine Learning Models for Mental Health Assessment Using Music Therapy

Shanmugasundaram, Kaviprasad (2024) Comparative Analysis of Machine Learning Models for Mental Health Assessment Using Music Therapy. Masters thesis, Dublin, National College of Ireland.

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Abstract

This project focuses on how machine learning can be used to estimate the levels of depression relying on the socio-economic and demographic variables, given the global rise of mental health disorders, such as, depression, anxiety, and OCD. The focus is in using individual data aimed to detect these issues at an earlier stage and deliver more tailored approaches that may help address these difficulties. KNN, Neural Networks, Decision Trees, Random Forests, and Gradient Boosting were identified as the models in the study. All these models were trained and assessed on a constrained structured dataset with prominent evaluation metrics including MSE and R² Score. The assessment exposed various findings regarding the prognostic capability and versatility of each model. Just like KNN, MLP has shown higher predictive accuracy with lowest MSE that makes this algorithm capable of capturing local data patterning. Neural Networks demonstrated the feature of capturing nonlinear relationship structure of the data. Some of the findings gives insights comparing many classifiers such as KNN outperforms the rest in accuracy and simplicity while Neural Networks outperforms in complex data features. On model selection, the project reminds the user of some dataset features and level of interpretability required when selecting suitable models. In doing so, this work creates a basis for further research to continue to build and refine the modeling of mental health issues and to extend the use of such modeling for practical endeavours in the early identification of possible disorders and the pursuit of appropriate intervention plans.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Anu UNSPECIFIED
Subjects:	M Music and Books on Music > M Music Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning R Medicine > RA Public aspects of medicine > RA790 Mental Health
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	05 Sep 2025 08:41
Last Modified:	05 Sep 2025 08:41
URI:	https://norma.ncirl.ie/id/eprint/8806

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