An alternative evaluation of post traumatic stress disorder with machine learning methods

Abstract

In the world we live in, people from different professions are at increased risk for depressive symptoms and posttraumatic stress disorder (PTSD) due to hard working or extreme environmental conditions. Accurate diagnosis and determining the causes are very important to solve these kinds of psychological problems. Machine learning (ML) techniques are gaining popularity in neuroscience due to their high diagnostic capability and effective classification ability. In this paper, alternative hybrid systems which allowed us to develop automatic classifiers for finding the Posttraumatic stress disorder (PTSD) patients are proposed and compared. With the proposed system, not only the PTSD individuals are classified by ML techniques such as sequential minimal optimization (SMO), multilayer perceptron (MLP), Naïve Bayes (NB) but also the important indications of patients' trauma are determined by three popular feature selection methods such as chi-square, principal component analysis (PCA) and correlation based-feature selection (CFS). The effectiveness of the proposed system is examined on a real world dataset. Due to obtained results we can estimate the individuals as PTSD or NONPTSD patients with 74-79% accuracy range, further to that instead of 39 features 7 features are remarked as the most critical symptoms for PTSD.

Multi-domain potential biomarkers for post-traumatic stress disorder (PTSD) severity in recent trauma survivors

“…Machine learning methods are particularly well-suited to address such computational challenges, as they can account for the intricate interrelation of many relevant factors 30 . Indeed, the last decade has shown an exponential increase in the use of machine learning for the study of posttraumatic stress, including both supervised and unsupervised approaches 1,[31][32][33] . While both approaches have shown varying success, supervised methods are limited by the accuracy of the prior knowledge they rely on, and unsupervised methods are limited in that subpopulations are not tied to specific questions of interest 34 .…”

Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach

“…Machine learning methods are sensitive enough to facilitate inference at the single-subject level, and can identify spatially distributed patterns in the brain that might be undetectable using group comparisons (Orrù et al ., 2012; Fu and Costafreda, 2013; Wolfers et al ., 2015). Recently, a growing number of studies have applied machine learning methods to neuroimaging data to predict and characterize psychiatric diseases (Bleich-cohen et al ., 2014; Mikolas et al ., 2016; Rive et al ., 2016), as well as PTSD (Gong et al ., 2014; Karstoft et al ., 2015; Liu et al ., 2015; Omurca and Ekinci, 2015; Galatzer-Levy et al ., 2017; Gradus et al ., 2017; Jin et al ., 2017; Saxe et al ., 2017). To date, however, no studies have examined the predictive validity of functional magnetic resonance imaging (fMRI) machine learning to classify PTSD and its dissociative subtype.…”

Identificando Sinais de Estresse Pós-traumático Utilizando Dados Fisiológicos e Técnicas de Regressão

“…O trabalho [Omurca and Ekinci 2015] propõe um sistema inteligente híbrido que combina algoritmos de classificação e seleção de atributos para classificar indivíduos com risco de desenvolver TEPT. Nas análises, foi utilizada uma base de dados com 391 indivíduos, dos quais 321 possuem risco de desenvolver TEPT e 70 não possuem risco.…”