Dynamic Expert System Design for the Prediction of Attention Deficit and Hyperactivity Disorder in Childhood

Year 2019, , 33 - 41, 31.01.2019

Hanife Göker , Hakan Tekedere

https://doi.org/10.17671/gazibtd.458102

Abstract

In
this study, for the first time, a Dynamic Expert System was developed to
predict attention deficit and hyperactivity impairment in childhood. In this
context, the decision-making process, which requires complex and experienced
field experts to diagnose the disease, has been transferred to the developed
expert system. The subject of the study was determined as prediction of
attention deficit and hyperactivity disorder, which is one of the most common
psychiatric disorders of childhood. The developed Dynamic Expert System
consists of three basic parts, which are the knowledge base, the inference
mechanism and the description unit. Data clusters are recorded as attributes
and records in the knowledge base. While attributes are determined by field
experts, records are composed of clinical patient data received from the Gazi
Hospital, Department of Pediatric Mental Health and Diseases. Ensuring the
dynamic renewal of the rule base is the most important characteristic of the
study using the Naive Bayes Algorithm in the inference mechanism of the
developed system. In this way, when the system encounters a new situation that
is not previously encountered, it can take advantage of the existing rules and
guess which class the rule belongs to. With real data, the system has been
trained; and its performance was tested. As a result of this study, accuracy
was determined to be 88.62%; precision was determined to be 89.2%, recall was
determined to be 88.6%, f-measure was determined to be 88.6% and ROC area value
was determined to be 89.8%. It was observed that the performance of the system
was quite high compared to the model performance criteria.

Keywords

expert system, machine learning, Naive Bayes Algorithm, early diagnosis, attention deficit, hyperactivity

Çocukluk Çağı Dikkat Eksikliği ve Hiperaktivite Bozukluğunun Öngörülmesine Yönelik Dinamik Uzman Sistem Tasarımı

Abstract

Bu
çalışma ile ilk defa çocukluk çağı dikkat eksikliği ve hiperaktivite
bozukluğunun öngörülmesine yönelik çocuk psikiyatristlerinin alan uzmanlığı
doğrultusunda tanı çıkarımı yapabilen bir dinamik uzman sistem tasarımı
geliştirilmiştir. Bu kapsamda hastalığın tanısına yönelik alan uzmanlarının
karmaşık ve deneyim gerektiren karar verme süreci, geliştirilen uzman sisteme
aktarılmıştır. Çalışmanın konusu gereksinim analizi yapılarak çocukluk çağının
en sık görülen psikiyatrik bozukluklarından olan dikkat eksikliği ve
hiperaktivite bozukluğu olarak seçilmiştir. Geliştirilen sistem bilgi tabanı,
çıkarım mekanizması ve açıklama birimi olmak üzere üç temel kısımdan
oluşmaktadır. Veri kümeleri, nitelikler ve kayıtlar olmak üzere bilgi tabanına
kaydedilmiştir. Nitelikler alan uzmanları (çocuk psikiyatristleri) tarafından
belirlenirken, kayıtlar Gazi Hastanesi Çocuk Ruh Sağlığı ve Hastalıkları
Anabilim Dalından alınan kliniksel hasta verilerinden oluşmaktadır.
Geliştirilen sistemin çıkarım mekanizması kısmında Naive Bayes algoritması
kullanılarak, kural tabanının dinamik olarak yenilenmesinin sağlanması
çalışmanın en önemli ayırt edici özelliğidir. Bu sayede sistem, daha önceden
kayıtlı olmayan yeni bir durum ile karşılaştığında; mevcut kurallardan
faydalanarak yeni kuralın hangi sınıfa ait olduğunu tahmin edebilmektedir.  Gerçek veriler ile sistem eğitilmiş ve
performansı test edilmiştir.  Çalışmanın
sonucunda, accuracy 88.62%, precision 89.2%, recall 88.6%, f-measure 88.6% ve
ROC area değeri 89.8 % bulunmuştur. Sistemin performansının model başarım
kriterlerine göre oldukça yüksek olduğu görülmüştür. 

Keywords

uzman sistem, makine öğrenmesi, Naive Bayes Algoritması, erken tanı, dikkat eksikliği, hiperaktivite

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