A Non-invasive Mental Health Risk Predictor Using Machine Learning Models Utilising Music Listening Habits

Aug 5, 2026

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Global mental health issues such as insomnia, anxiety, and depression are increasing, yet stigma often discourages people from seeking support. Research has suggested links between music listening habits and mental health, enabling predictive approaches. This study proposes a non-invasive framework that combines features derived from music listening patterns with demographic and lifestyle data to train machine learning models. Random Forest reaches 76.35% accuracy, supporting prediction of depression, anxiety, and insomnia across age groups.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Abstract

Introduction

Music, mood, and mental states

Data collection and modelling approach

Related Works

Communications of the IIMA Manuscript 1476 A Non-invasive Mental Health Risk Predictor Using Machine Learning Models Utilising Music Listening Habits Follow this and additional works at: [https://scholarworks.lib.csusb.edu/ciima](https://scholarworks.lib.csusb.edu/ciima) Part of the Management Information Systems Commons A Non-invasive Mental Health Risk Predictor Using Machine Learning Models Utilising Music Listening Habits Murtala A. Buhari University of Salford [M.a.buhari@edu.salford.ac.uk](M.a.buhari@edu.salford.ac.uk) Nimesh V. Vinod University of Salford [N.vismayavinod@edu.salford.ac.uk](N.vismayavinod@edu.salford.ac.uk) ABSTRACT Globally, there has been a rise in mental health issues such as insomnia, anxiety, and depression. However, the stigma that is associated with such a diagnosis makes individuals not want to seek help. Recent research has explored the relationship between music listening habits and mental health status, offering promising insights into the potential of leveraging this data for predictive modelling. This research proposes a non-invasive approach that integrates features extracted from music listening patterns including demographic and lifestyle data to build machine learning models that detect mental health conditions such as insomnia, depression and anxiety levels The results show that Random Forest achieved an accuracy of 76.35%, which highlights the potential of using music listening habits to predict mental health states. The findings of this study provide valuable insight into the relationship between music and mental health predictors- namely depression, anxiety and insomnia across different age groups. Keywords: e-health; mental health prediction; machine learning, music therapy. INTRODUCTION Music is a fundamental aspect of human experience, analogous to speech in its capacity to convey mental states. Historically, music has served as a lens through which various cultural, psychological, and emotional dimensions of society can be observed. At the core of this research is the assumption that music plays an integral part of daily life for many, and might reflect underlying emotional and mental states. Music's profound impact on emotions and mood is well- documented through the use of music therapy, which has been shown to improve various mental illnesses such as depression (Gujar & Reha, 2023) . Despite growing interest in utilising music as a treatment for various mental illnesses, the literature remains limited regarding the relationship between music and three prevalent conditions: anxiety, depression, and insomnia. This paper aims to employ machine-learning techniques to improve models' predictive capability and identify significant relationships among various features, including demographics, genres, and other reported factors. The first phase will collect extensive datasets comprising individuals' music listening histories, demographic information, and self-reported mental health statuses. This data is gleaned from music streaming services, which provide detailed user logs of genre preferences, song choices, listening frequencies, and durations. Additionally, mental health data is obtained through standardized psychological assessments and questionnaires. Machine learning models, such as neural networks, logistic regression, and support vector machines, are employed to train these datasets. The models are trained to detect patterns and associations between music listening behaviours and reported mental health issues. Preliminary studies have shown correlations where preferences for certain music types correlate with specific psychological profiles (Chanda & Levitin, 2013) . For instance, a preference for high-energy, aggressive music has been linked to higher stress levels, whereas classical or soft music is often associated with lower stress levels (Vuoskoski & Eerola, 2011) . The confluence of digital health technology, particularly machi

FAQs

How does the proposed system predict mental health risks?

It uses non-invasive features extracted from music listening patterns, combined with demographic and lifestyle information, to train machine learning models that detect mental health conditions.

Which machine learning model performed best in the study?

Random Forest achieved an accuracy of 76.35%, indicating strong potential for predicting mental health states from listening habits.

What mental health conditions does the research focus on?

The study targets depression, anxiety, and insomnia levels, aiming to capture meaningful relationships across different age groups.

Aug 5, 2026

Upload by Quinn