Machine learning models, including deep learning, support vector machines, and random forests, were proposed to forecast mental well-being using behavioral, psychological, and environmental data.
Can machine learning models accurately forecast mental well-being using behavioral, psychological, and environmental data?
Machine learning models using diverse datasets may offer a novel approach to forecasting mental well-being compared to traditional subjective measures.
Mental well-being is critical to an individual's health and quality of life. It encompasses emotional, psychological, and social dimensions, making it a complex and multifaceted construct. Traditionally, assessing and forecasting mental well-being has relied on self-report measures, clinical evaluations, and surveys, which can be subjective, time-consuming, and limited scope. To overcome these drawbacks and offer more precise and timely insights into mental well-being, this study presents a novel approach that uses the power of machine learning. This has been achieved by creating a comprehensive dataset containing various variables relevant to mental health. These variables include behavioral traits like exercise routines, sleep patterns, and social interactions and psychological traits like mood, stress levels, and emotional states. Environmental factors, such as geographic location, climate, and accessibility to mental health care, are also considered. A comprehensive understanding of the elements affecting mental well-being is made possible by this diversified dataset. The data is analyzed using machine learning models, such as deep learning neural networks, support vector machines, and random forests. These models were selected since they can capture intricate correlations and patterns in data, making them suitable for forecasting mental health.
Rayan et al. (Thu,) conducted a other in Mental well-being. Machine learning models was evaluated on Forecasting mental well-being. Machine learning models, including deep learning, support vector machines, and random forests, were proposed to forecast mental well-being using behavioral, psychological, and environmental data.
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