Nagy, Á and Dombi, József and Fülep, M P and Rudics, E and Hompoth, Emőke Adrienn and Szabó, Zoltán and Viharos, Zsolt János and Hoang, Anh Tuan and Maczák, Bálint and Vadai, Gergely and Gingl, Zoltán and Bilicki, Vilmos and Szendi, István (2023) The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder. SENSORS, 23 (2). ISSN 1424-8220 10.3390/s23020958

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Abstract

(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way.

Item Type:	Article
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Research Laboratory on Engineering & Management Intelligence
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	28 Feb 2023 12:30
Last Modified:	11 Sep 2023 15:00
URI:	https://eprints.sztaki.hu/id/eprint/10505

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