Machine Learning Guided Lyric-analysis Peer Support Intervention for Psychological Distress in African Population: A BOM Conceptualized Framework

Background: Sub-Saharan Africa faces a significant burden of mental health challenges, including depression and anxiety, particularly among vulnerable populations such as women and individuals living with HIV. This study proposes a machine learning-guided Lyric Analysis Peer Support Intervention (LAPSI) to predict psychological distress and inform interventions. The study utilizes machine learning to generate risk profiles, presenting an innovative application in identifying psychological distress risk factors for developing targeted interventions. Objective: The objective of this study is to utilize machine learning models to generate risk profiles that predict psychological distress among HIV-positive and HIV-negative populations and to use these profiles to guide thematic development for LAPSI, ensuring confidentiality and informed consent. Methods: The study aims to leverage machine learning algorithms such as Logistic Regression and Random Forest to generate risk profiles and to analyze demographic, socio-economic, and psychological factors, including HIV status, age, education, and employment. Introducing a novel application of machine learning in the identification of psychological distress risk factors for intervention development while adhering to ethical standards for handling sensitive data like HIV status. Prior to data collection, ethical approval will be obtained, ensuring participant confidentiality and informed consent. Themes generated from the risk profiles will inform song selection and peer support intervention for LAPSI. Results: The machine learning models are expected to highlight education, employment status, and marital status as critical predictors of psychological distress. Geographical and demographic variables, such as district and age, are hypothesized to play a significant role in predicting distress among both HIV-positive and HIV-negative cohorts. Conclusion: This study posits that machine learning models will provide actionable insights into predicting psychological distress, enabling targeted interventions through LAPSI. This short communication argues for integrating LAPSI into health policies and calls upon health policy actors to recognize its potential in addressing the mental health crisis in Sub-Saharan Africa, advocating for partnerships with local communities, healthcare providers, and mental health advocates to tailor and implement this intervention effectively.