Optimizing linear models via sinusoidal transformation for boosted machine learning in medicine

Abstract EN

Background : Machine learning relies on a hybrid of analytics, including regression analyses.

There have been no attempts to deploy a sinusoidal transformation of data to enhance linear regression models.

Objectives : We aim to optimize linear models by implementing sinusoidal transformation to minimize the sum of squared error.

Methods : We implemented non-Bayesian statistics using SPSS and MatLab.

We used Excel to generate 30 trials of linear regression models, and each has 1, 000 observations.

We utilized SPSS linear regression, Wilcoxon signed-rank test, and Cronbach's alpha statistics to evaluate the performance of the optimization model.

Results : Thesinusoidal transformation succeeded by significantly reducing the sum of squared errors (P-value < 0.

001).

Inter-item reliability testing confirmed the robust internal consistency of the model (Cronbach's alpha = 0.

999).

Conclusion : Ouroptimization model is valuable for high-impact research based on linear regression.

It can reduce the computational processing demands for powerful real-time and predictive analytics of big data.