Predictive Analysis of Sleeps Impact on Mental Health

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.