Bilişim Teknolojileri Dergisi 1307-9697 2147-0715 Gazi Üniversitesi 10.17671/gazibtd.458102 Computer Software Bilgisayar Yazılımı Dynamic Expert System Design for the Prediction of Attention Deficit and Hyperactivity Disorder in Childhood Çocukluk Çağı Dikkat Eksikliği ve Hiperaktivite Bozukluğunun Öngörülmesine Yönelik Dinamik Uzman Sistem Tasarımı Göker Hanife Tekedere Hakan GAZİ ÜNİVERSİTESİ 01 31 2019 12 1 33 41 09 07 2018 12 13 2018 Copyright © 2008, Bilişim Teknolojileri Dergisi 2008 Bilişim Teknolojileri Dergisi

Inthis study, for the first time, a Dynamic Expert System was developed topredict attention deficit and hyperactivity impairment in childhood. In thiscontext, the decision-making process, which requires complex and experiencedfield experts to diagnose the disease, has been transferred to the developedexpert system. The subject of the study was determined as prediction ofattention deficit and hyperactivity disorder, which is one of the most commonpsychiatric disorders of childhood. The developed Dynamic Expert Systemconsists of three basic parts, which are the knowledge base, the inferencemechanism and the description unit. Data clusters are recorded as attributesand records in the knowledge base. While attributes are determined by fieldexperts, records are composed of clinical patient data received from the GaziHospital, Department of Pediatric Mental Health and Diseases. Ensuring thedynamic renewal of the rule base is the most important characteristic of thestudy using the Naive Bayes Algorithm in the inference mechanism of thedeveloped system. In this way, when the system encounters a new situation thatis not previously encountered, it can take advantage of the existing rules andguess which class the rule belongs to. With real data, the system has beentrained; and its performance was tested. As a result of this study, accuracywas determined to be 88.62%; precision was determined to be 89.2%, recall wasdetermined to be 88.6%, f-measure was determined to be 88.6% and ROC area valuewas determined to be 89.8%. It was observed that the performance of the systemwas quite high compared to the model performance criteria.

Buçalışma ile ilk defa çocukluk çağı dikkat eksikliği ve hiperaktivitebozukluğunun öngörülmesine yönelik çocuk psikiyatristlerinin alan uzmanlığıdoğrultusunda tanı çıkarımı yapabilen bir dinamik uzman sistem tasarımıgeliştirilmiştir. Bu kapsamda hastalığın tanısına yönelik alan uzmanlarınınkarmaşık ve deneyim gerektiren karar verme süreci, geliştirilen uzman sistemeaktarılmıştır. Çalışmanın konusu gereksinim analizi yapılarak çocukluk çağınınen sık görülen psikiyatrik bozukluklarından olan dikkat eksikliği vehiperaktivite bozukluğu olarak seçilmiştir. Geliştirilen sistem bilgi tabanı,çıkarım mekanizması ve açıklama birimi olmak üzere üç temel kısımdanoluşmaktadır. Veri kümeleri, nitelikler ve kayıtlar olmak üzere bilgi tabanınakaydedilmiştir. Nitelikler alan uzmanları (çocuk psikiyatristleri) tarafındanbelirlenirken, kayıtlar Gazi Hastanesi Çocuk Ruh Sağlığı ve HastalıklarıAnabilim Dalından alınan kliniksel hasta verilerinden oluşmaktadır.Geliştirilen sistemin çıkarım mekanizması kısmında Naive Bayes algoritmasıkullanılarak, kural tabanının dinamik olarak yenilenmesinin sağlanmasıçalışmanın en önemli ayırt edici özelliğidir. Bu sayede sistem, daha öncedenkayıtlı olmayan yeni bir durum ile karşılaştığında; mevcut kurallardanfaydalanarak yeni kuralın hangi sınıfa ait olduğunu tahmin edebilmektedir. Gerçek veriler ile sistem eğitilmiş veperformansı test edilmiştir. Çalışmanınsonucunda, accuracy 88.62%, precision 89.2%, recall 88.6%, f-measure 88.6% veROC area değeri 89.8 % bulunmuştur. Sistemin performansının model başarımkriterlerine göre oldukça yüksek olduğu görülmüştür.

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