Izvorni znanstveni članak

https://doi.org/10.24869/psyd.2025.46

Evaluating machine learning algorithms for prediction of treatment response for sleep disturbances in patients with schizophrenia: A post-hoc analysis from a randomized controlled trial

Archana Mishra ; All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India
Rituparna Maiti ; All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India
Monalisa Jena ; All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India
Anand Srinivasan ; All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India

Sažetak

Background: A post-hoc analysis was planned to create and compare machine learning algorithms to predict treatment responses
to sleep disturbances in patients with schizophrenia.
Subjects and Methods: This post-hoc analysis was done on a randomized controlled trial (NCT03075657), studying the effect
of add-on ramelteon on sleep and circadian rhythm disturbances in 120 patients with schizophrenia. We created models using random
forest, k-nearest neighbors, extreme gradient boosting machine, R part Classification and regression trees and logistic regression algorithms.
R language with mlbench, caret, MASS, rPART packages were used. Box plot and dot plot were plotted to visualize comparisons
among the models.
Results: The logistic regression algorithm was found to be the best-fit model with a specificity of 0.93 and sensitivity of 0.45, and
ROC 0.78. Predominant symptom domain (positive or negative), urinary melatonin and global PSQI score at baseline were the most
important variables when plotted in terms of mean decrease accuracy. These variables contributed significantly to the final model in
the logistic regression algorithm, and the accuracy of this algorithm was found to be 90% for prediction.
Conclusions: Machine learning models are an emerging trend in clinical research and should be translated into clinical practice.
The logistic regression model predicted responders with 90% accuracy.

Ključne riječi

Schizophrenia; sleep disorder; machine learning

Hrčak ID:

332226

URI

https://hrcak.srce.hr/332226

Datum izdavanja:

29.5.2025.

Posjeta: 338 *