Advanced Model Approach for the Progression and Early Diagnosis of Insomnia

Objective: Insomnia is a common psychiatric condition characterized by persistent difficulties falling or staying asleep despite having adequate opportunities and conditions.Seven core parameters, as measured by the Insomnia Severity Index, determine the risk of insomnia.A cubic polynomial regression model was developed based on the baseline parameters to forecast insomnia severity. Materials and Methods:The model incorporates quadratic and cubic terms to enhance accuracy by capturing nonlinear dependencies between variables.Least squares regression and ridge regularization improve stability and avoid overfitting.Results: The model's performance was evaluated using mean squared error (MSE) and coefficient of determination R-squared (R 2 ), achieving an accuracy that effectively represents trends in insomnia severity.The results indicate that the cubic polynomial model significantly enhances prediction reliability, reducing the MSE value from simpler polynomial approaches to 0.0018 and increasing the R 2 value to 0.98. Conclusion:The performance results demonstrate that the model's prediction of insomnia severity largely aligns with the actual observation data.This demonstrates that the model can accurately analyze sleep disorders and make dependable predictions.These findings suggest that advanced polynomial regression may be a valuable tool in sleep analysis, enabling the prediction of insomnia severity.