Detection of Autistic Spectrum Disorder Using Artificial Neural Network

Year 2023, Volume: 23 Issue: 4, 955 - 961, 31.08.2023

Şeyma Nur Özdemir , Kazım Yıldız

https://doi.org/10.35414/akufemubid.1239360

Abstract

Autistic Spectrum Disorder (ASD) is a neuro-developmental disorder that is congenital or manifests with a delay in social relations and physiological development at an early age, and also causes problems in communication. It is possible to reduce the effect of the disease on individuals with early diagnosis. However, detecting ASD at an early age requires time and cost. In the studies conducted in recent years, it is seen that there is a serious increase in ASD cases. In order to prevent this increase, decision support systems should be established for early diagnosis. It is important to develop decision support models to diagnose ASD, especially for children aged 12-36 months. In this study, a model was developed that can help in detecting ASD with high accuracy for 12-36 months old children. The data set used in the created model was collected from the mobile application named ASDTests developed by Thabtah. In the estimation phase, four different machine learning algorithms which are support vector machine, Naive Bayes,Random Forest and Artificial Neural Network were used. In the classification process, high success rate was obtained with artificial neural network, random forest classifier.

Keywords

Autism Spectrum Disorder, Early Diagnosis System, Random Forest, Artificial Neural Network

Otistik Spectrum Bozukluğunun Yapay Sinir Ağları ile Tespiti

Abstract

Otistik Spektrum Bozukluğu (OSB), doğuştan gelen yada yaşamın ilk yaşlarında sosyal ilişkilerde ve fizyolojik gelişimde gecikme ile kendini gösteren ve aynı zamanda iletişimde sorunlara neden olan nöro-gelişimsel bir bozukluktur. Hastalığın bireyler üzerinde etkisinin erken tanı ile azaltılması mümkündür. Ancak OSB’yi erken yaşta tespit etmek zaman ve maliyet gerektirmektedir. Son yıllarda yapılan çalışmalarda OSB vakalarında ciddi bir artış olduğu görülmektedir. Bu artışı önlemek için erken tanı için karar destek sistemleri oluşturulmalıdır. Özellikle 12-36 aylık çocuklar için OSB tanısı koymak için karar destek modellerinin geliştirilmesi önem taşımaktadır. Bu çalışmada 12-36 aylık çocuklar için yüksek doğrulukta OSB tespitinde yardımcı olabilecek bir model geliştirilmiştir. Oluşturulan modelde kullanılan veri seti Thabtah tarafından geliştirilen ASDTests isimli mobil uygulamadan toplanmıştır. Tahminleme aşamasında destek vektör makinesi, Naive Bayes, rasgele orman , yapay sinir ağları olmak üzere dört farklı makine öğrenimi algoritması kullanılmıştır. Sınıflandırma sürecinde yapay sinir ağları, rasgele orman sınıflandırıcı ile yüksek başarı oranı elde edilmiştir.

Keywords

Otistik Spektrum Bozukluğu, Erken Tanı Sistemi, Rasgele Orman, Yapay Sinir Ağları

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