Abstract
Objective Alexithymia, as a fundamental notion in the diagnosis of psychiatric disorders, is characterized by deficits in emotional processing and, consequently, difficulties in emotion recognition. Traditional tools for assessing alexithymia, which include interviews and self-report measures, have led to inconsistent results due to some limitations as insufficient insight. Therefore, the purpose of the present study was to propose a new screening tool that utilizes machine learning models based on the scores of facial emotion recognition task. Method In a cross-sectional study, 55 students of the University of Tabriz were selected based on the inclusion and exclusion criteria and their scores in the Toronto Alexithymia Scale (TAS-20). Then, they completed the somatization subscale of Symptom Checklist-90 Revised (SCL-90-R), Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II), and the facial emotion recognition (FER) task. Afterwards, support vector machine (SVM) and feedforward neural network (FNN) classifiers were implemented using K-fold cross validation to predict alexithymia, and the model performance was assessed with the area under the curve (AUC), accuracy, sensitivity, specificity, and F1-measure. Results The models yielded an accuracy range of 72.7–81.8% after feature selection and optimization. Our results suggested that ML models were able to accurately distinguish alexithymia and determine the most informative items for predicting alexithymia. Conclusion Our results show that machine learning models using FER task, SCL-90-R, BDI-II, and BAI could successfully diagnose alexithymia and also represent the most influential factors of predicting it and can be used as a clinical instrument to help clinicians in diagnosis process and earlier detection of the disorder.
Highlights
Alexithymia could be briefly described as emotional blindness [1] and refers to the difficulty in expressing and recognizing emotional states [2]
Given the role of alexithymia in psychopathology and psychiatry, as well as the association of alexithymia with facial emotion recognition (FER) defects, the present study aimed to investigate the relationship between FER deficits, somatization, depression, and anxiety with alexithymia levels. e level of alexithymia (TAS-20) was predicted using the FER task dataset and the somatization subscale of SCL-90-R, Beck Depression Inventory-II (BDI-II), and Beck Anxiety Inventory (BAI) questionnaires, which had been implemented in the machine learning (ML) methods of the artificial neural network (ANN) and support vector machine (SVM)
Both the SVM and feedforward neural network (FNN) classification algorithms were able to distinguish between Alex and HC. e confusion matrix for the classification models is shown in Table 2. e confusion matrix shown in Table 2 represents the values of performance for the final classification model using two different classifiers (FNN and SVM), two different evaluation methods without/with feature selection and hyperparameter tuning
Summary
Alexithymia could be briefly described as emotional blindness [1] and refers to the difficulty in expressing and recognizing emotional states [2]. Alexithymic individuals misinterpret the somatic symptoms of emotional arousal, try to express their emotional distress through physical complaints, and seek treatment for their physical symptoms [3]. It is argued that alexithymic individuals exhibit difficulties in understanding and regulating their emotions [4]. Alexithymia, and the subscale of “difficulty in identifying feelings,” is associated with various psychiatric disorders [6,7,8,9,10]. Follow-up studies show that chronic alexithymia is consistently associated with depression and various symptoms of psychological disorders such as cluster-c personality disorders [11,12,13].
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