Anksiyete Bozukluğunda Makine Öğrenmesi Teknikleri

Abstract

Son yıllarda psikiyatrik hastalıkların teşhisinin zamanlamasını, duyarlılığını ve kalitesini iyileştirmek için yapay zeka tabanlı uygulamalar geliştirilmekte ve kullanılmaktadır. Anksiyete bozukluğu olan deneklerin değerlendirilmesinde yapay zeka tekniklerinin kullanımına ilişkin mevcut literatürü gözden geçirmeyi amaçlanmaktadır. Anksiyete bozukluklarının ana kategorilerinden biri olan; ayrılık kaygısı bozukluğu, genelleşmiş kaygı bozukluğu, panik bozukluğu ve sosyal kaygı bozukluğu DSM-5 (Ruhsal Bozuklukların Tanısal ve İstatistiksel El Kitabı) ile ilgili 2015-2021 yılları arasındaki veri tabanları araştırılmıştır. Bu çalışmalarda kullanılan 30 farklı teknik belirlenmiştir. Yapılan çalışmalarda birden fazla algoritma ile karşılaştırmalar yapılmıştır. Bu algoritmalar arasında en çok kullanılan makine öğrenmesi yönteminde Rastgele Orman Algoritması görülmüştür. Ayrıca en iyi doğruluk performansı Rastgele Orman Algoritması'nda gözlemlenmiştir. Bu makale, kaygı üzerine yapılan bu son araştırma çalışmalarını eleştirel bir şekilde analiz etmektedir. Anksiyete hastalarından elde edilen verilerin klinik heterojenliği göz önüne alındığında, yapay zeka tekniklerinin tanı, kişiselleştirilmiş tedavi ve prognoz gibi alanlarda klinisyenlere ve araştırmacılara önemli bilgiler sağlayabileceği sonucuna varılmıştır.

Keywords

• Anksiyete Bozukluğu
• Makine Öğrenmesi
• Panik Bozukluk
• Psikiyatrik Bozukluklar
• Sosyal Anksiyete Bozukluğu
• Rastgele Orman Sınflandırıcılar

Machine Learning Techniques for Anxiety Disorder

Abstract

In recent years, artificial intelligence based applications have been improved and used to improve the timing, sensitivity and quality of diagnosis of psychiatric diseases. We aim to review the existing literature on the use of artificial intelligence techniques in the assessment of subjects with anxiety disorder. We searched databases about DSM-5 (Diagnostic and Statistical Manual of Mental Disorders) one of the main categories of anxiety disorders; Separation Anxiety Disorder, Generalized Anxiety Disorder, Panic Disorder and Social Anxiety Disorder between 2015-2021. We identified 30 different techniques on these works. Comparisons have been made with more than one algorithm in the studies. The Random Forest Algorithm has been seen in the most used machine learning method among these algorithms. In addition, the best accuracy performance has been observed in the Random Forest Algorithm. This article critically analyzes these recent research studies on anxiety. Considering the clinical heterogeneity of the data obtained from anxiety patients, we conclude that artificial intelligence techniques can provide important information to clinicians and researchers in areas such as diagnosis, personalized treatment, and prognosis.

Keywords

• Anxiety disorders
• Machine Learning
• Panic Disorder
• Psychiatric Disorders
• Social Anxiety Disorder
• Random Forest Classifier

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