Abstract:
The significant influence of sleep on cognitive and emotional state is acknowledged by
this research, which explores the complex relationship among sleep patterns & mental
health outcomes. Through the identification of critical sleep characteristics predictive
of mental health issues and the development of precise predictive models to identify
individuals at increased risk of depression based on extensive sleep data, the study fills
in current gaps in the literature.
Numerous predictive models are used in the study, such as Convolutional Neural
Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU),
Support Vector Machine (SVM), and Logistic Regression. With a 53.24% predictive
accuracy, Logistic Regression is a useful tool for predicting mental health, and other
sophisticated models such as SVM, GRU, LSTM, CNN, and RNN offer similar
insights. The study highlights the need for more investigation and focused therapies by
illuminating the intricate relationship among sleep patterns and mental health.
The thorough examination of sleep data in conjunction with sophisticated predictive
models advances our knowledge of the variables affecting mental health outcomes. The
results emphasize how early identification and treatment can lessen the effects of mental
health problems. The work adds to our knowledge and proposes new lines of inquiry,
such as modeling predictions for high-risk persons and moral issues with the use of
mental health services.
To sum up, this study provides important new understandings of the complex interplay
among sleep and mental health, setting the stage for proactive, individualized mental
health therapies. The prediction models created in this work aid in the identification of
those who are at risk and offer a means of developing more focused interventions and
potent therapies. However, the effective application of predictive analytics in mental
health treatment will require continued investigation, improvement, and ethical
considerations. The study paves the way for more research in this developing sector and
is a step toward transforming treatment practices for the improvement of mental health
outcomes.
