Travma Sonrası Stres Bozukluğunun Derin Öğrenme Yöntemleri ile Tespiti

Year 2022, Volume: 9 Issue: 4, 1274 - 1281, 31.12.2022

Engin Seven , Cansın Turguner , Muhammed Ali Aydın

https://doi.org/10.31202/ecjse.1133463

Abstract

Travma sonrası stres bozukluğu (TSSB), kişinin yaşadığı travmatik bir olay sonrasında ruhsal ve fiziksel hayatını olumsuz yönde etkileyen psikiyatrik bir sorundur. Hastalığın erken aşamada fark edilip tedavi edilmemesi bipolar bozukluk, anksiyete veya intihar eğilimi gibi olumsuz sonuçlar ortaya çıkarabilmektedir. TSSB nin erken aşamada tespiti için yapay zeka temelli bir model geliştirilmiştir. Yapılan çalışmada K-En Yakın Komşu algoritması, Destek Vektör Makineleri, Karar Ağaçları, Gaus Naive Bayes ve Yapay Sinir Ağları kullanılmış Covid-19 pandemisi devam ederken tıp öğrencilerinden toplanan veri seti üzerinde testler gerçekleştirilmiştir. Yapılan çalışmada doğruluk, kesinlik, hassasiyet ve f1 skoru değerleri karşılaştırmalı incelenmiştir. Yapay sinir ağları 0,987 doğruluk oranı ile en iyi sonucu elde etmiştir. Ayrıca 0,966 f1 skoru ile yapay sinir ağları en iyi TSSB tahmininde bulunmuştur.

Keywords

Travma sonrası stres bozukluğu, Derin Öğrenme, Akıllı teşhis, Özellik Seçimi

Detection of Post Traumatic Stress Disorder with Deep Learning Methods

Abstract

Post-traumatic stress disorder (PTSD) is a psychiatric problem that negatively affects a person's mental and physical life after a traumatic event. If the disease is not recognized and treated at an early stage, negative consequences such as bipolar disorder, anxiety or suicidality can occur. An artificial intelligence-based model has been developed for the early detection of PTSD. In the study, K-Nearest Neighbor algorithm, Support Vector Machines, Decision Trees, Gaus Naive Bayes and Artificial Neural Networks were used and tests were carried out on the dataset collected from medical students during the Covid-19 pandemic. In the study; accuracy, precision, recall and f1 score values were examined comparatively. Artificial neural networks achieved the best result with an accuracy rate of 0,987. In addition, artificial neural networks found the best PTSD prediction with an f1 score of 0,966.

Keywords

Post-traumatic stress disorder, Deep learning, Intelligent diagnosis, Feature Selection

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