APPLICATION OF MACHINE LEARNING TECHNIQUES FOR DISTINGUISHING SCHIZOPHRENIA PATIENTS FROM HEALTHY SUBJECTS USING FRONTAL LOBE FUNCTIONS ASSESSMENTS

Denisas Dankinas, Elzbieta Budginaitė, S. Mėlynytė, A. Šiurkutė, K. Dapsys
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Abstract

Machine learning (ML) represents a set of artificial in­telligence techniques that can assist in recognition of schizophrenia by classifying a person as belonging to either clinical or healthy subjects group. In the current study, we employed cognitive assessments of frontal lobe functions (the deficit of which is one of the prominent features of schizophrenia) for the training of ML models. Dataset for this research was engaged from our previous studies of two frontal lobe functions (response prepara­tion and inhibition of imitation) in case of schizophrenia. According to our knowledge, all previous cognitive ML schizophrenia studies used only the data from standard neuropsychological test batteries. Nevertheless, we em­ployed the cognitive data assessed with special experi­mental techniques that allowed us to engage Intra-indi­vidual reaction time variability (IIV) together with the classical reaction time (RT) assessment. It is important to emphasize that IIV is a cognitive measurement parameter that received vast attention of neuroscientists during the two last two decades and showed higher results in distin­guishing of schizophrenia patients from healthy subjects than standard RT in the number of studies. The result revealed statistically significant accuracy for all ML models in current study. Moreover, ML classifier with the highest accuracy outperformed the accuracy of a number of best models previously trained with standard neuropsychological test batteries datasets. Thus, cogni­tive experimental assessments of frontal lobe functions (response preparation and inhibition of imitation) can be effectively employed in developing of ML classifiers for distinguishing schizophrenia patients from healthy subjects.
应用机器学习技术通过额叶功能评估来区分精神分裂症患者和健康受试者
机器学习(ML)代表了一组人工智能技术,可以通过将一个人分为临床或健康受试者组来帮助识别精神分裂症。在目前的研究中,我们使用额叶功能的认知评估(其缺陷是精神分裂症的突出特征之一)来训练ML模型。本研究的数据集来自我们之前对精神分裂症患者额叶两种功能(反应准备和模仿抑制)的研究。据我们所知,所有以前的认知ML精神分裂症研究只使用标准神经心理学测试电池的数据。然而,我们采用了特殊实验技术评估的认知数据,使我们能够将个体内反应时间变异性(iv)与经典反应时间(RT)评估结合起来。需要强调的是,IIV是一个认知测量参数,在过去二十年中受到神经科学家的广泛关注,并且在区分精神分裂症患者和健康受试者方面显示出比标准RT更高的研究数量。结果显示,本研究中所有ML模型的准确性具有统计学意义。此外,具有最高准确性的ML分类器优于先前使用标准神经心理学测试电池数据集训练的许多最佳模型的准确性。因此,额叶功能(反应准备和模仿抑制)的认知实验评估可以有效地用于开发区分精神分裂症患者和健康受试者的ML分类器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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