Comparison of Different Classifiers Through Feature Selection in The Diagnosis of Autism Spectrum Disorder

Sinem Koç; Onur Sevli

Makale detayları

Comparison of Different Classifiers Through Feature Selection in The Diagnosis of Autism Spectrum Disorder

Yazarlar:

Sinem Koç | Onur Sevli

Yayıncı:

Fırat University

Ana Başlıklar:

Makine Öğrenme (Diğer)

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.37 Issue 1, pp.61-74

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001334860

pISSN:

1308-9072

Anahtar kelimeler:

Özet:

 Autism Spectrum Disorder ASD is a complex neurodevelopmental condition with a marked distribution of difficulties in social interaction and communication, repetitive processing and emotional problems. It is manifested by difficulties with individuals social interaction, the development of communication and difficulties with certain patterns of behavior. It is observed that autism usually starts in early childhood and gains prominence in this period. Early diagnosis is important in autism because early diagnosis allows treatment to start early. In addition to traditional methods for diagnosing ASD, machine learning methods, which have applications in many different fields with their stable inferences, are now used to increase diagnostic success. In this study, SelectKBest feature selection method was applied using a dataset consisting of 18 feature variables 17 input and 1 target and classification processes were performed with four different machine learning algorithms K-Nearest Neighbor, Logistic Regression, Naive Bayes, Support Vector Machines. Classification performance was evaluated with metrics such as accuracy, sensitivity, specificity and F1 score. After feature selection, Support Vector Machines and Logistic Regression algorithms achieved 100 accuracy, while K-Nearest Neighbor and Naive Bayes algorithms achieved 94.7 and 96.7 accuracy, respectively. When no feature selection was performed, the highest accuracy rate was recorded as 96.2. The results show that feature selection significantly improves the classification performance of machine learning algorithms. These results demonstrate the applicability and accuracy of machine learning methods in the diagnosis of ASD and provide an important contribution to improve the diagnostic process.

 Autism Spectrum Disorder ASD is a complex neurodevelopmental condition with a marked distribution of difficulties in social interaction and communication, repetitive processing and emotional problems. It is manifested by difficulties with individuals social interaction, the development of communication and difficulties with certain patterns of behavior. It is observed that autism usually starts in early childhood and gains prominence in this period. Early diagnosis is important in autism because early diagnosis allows treatment to start early. In addition to traditional methods for diagnosing ASD, machine learning methods, which have applications in many different fields with their stable inferences, are now used to increase diagnostic success. In this study, SelectKBest feature selection method was applied using a dataset consisting of 18 feature variables 17 input and 1 target and classification processes were performed with four different machine learning algorithms K-Nearest Neighbor, Logistic Regression, Naive Bayes, Support Vector Machines. Classification performance was evaluated with metrics such as accuracy, sensitivity, specificity and F1 score. After feature selection, Support Vector Machines and Logistic Regression algorithms achieved 100 accuracy, while K-Nearest Neighbor and Naive Bayes algorithms achieved 94.7 and 96.7 accuracy, respectively. When no feature selection was performed, the highest accuracy rate was recorded as 96.2. The results show that feature selection significantly improves the classification performance of machine learning algorithms. These results demonstrate the applicability and accuracy of machine learning methods in the diagnosis of ASD and provide an important contribution to improve the diagnostic process.

 Autism Spectrum Disorder ASD is a complex neurodevelopmental condition with a marked distribution of difficulties in social interaction and communication, repetitive processing and emotional problems. It is manifested by difficulties with individuals social interaction, the development of communication and difficulties with certain patterns of behavior. It is observed that autism usually starts in early childhood and gains prominence in this period. Early diagnosis is important in autism because early diagnosis allows treatment to start early. In addition to traditional methods for diagnosing ASD, machine learning methods, which have applications in many different fields with their stable inferences, are now used to increase diagnostic success. In this study, SelectKBest feature selection method was applied using a dataset consisting of 18 feature variables 17 input and 1 target and classification processes were performed with four different machine learning algorithms K-Nearest Neighbor, Logistic Regression, Naive Bayes, Support Vector Machines. Classification performance was evaluated with metrics such as accuracy, sensitivity, specificity and F1 score. After feature selection, Support Vector Machines and Logistic Regression algorithms achieved 100 accuracy, while K-Nearest Neighbor and Naive Bayes algorithms achieved 94.7 and 96.7 accuracy, respectively. When no feature selection was performed, the highest accuracy rate was recorded as 96.2. The results show that feature selection significantly improves the classification performance of machine learning algorithms. These results demonstrate the applicability and accuracy of machine learning methods in the diagnosis of ASD and provide an important contribution to improve the diagnostic process.

 Otizm Spektrum Bozukluğu OSB, sosyal etkileşim ve iletişimde zorluklar, tekrarlayıcı işlemler ve duygusal sorunlar gibi belirgin bir dağılıma sahip karmaşık bir nörogelişimsel durumdur. Bireylerin sosyal etkileşimi, iletişimin gelişmesi ve belirli davranış kalıpları üzerindeki zorluklarla birlikte kendini gösterir. Otizmin genellikle erken çocukluk döneminde başladığı ve bu dönemde belirginlik kazandığı gözlemlenmektedir. Otizmde erken teşhis önemlidir; çünkü erken tanı ile tedavinin erken başlaması mümkündür. OSB sorununun teşhisi için geleneksel yöntemlere ek olarak, günümüzde istikrarlı çıkarımları ile farklı pek çok alanda uygulamaları olan makine öğrenmesi yöntemleri teşhis başarısını arttırmak amacıyla kullanılmaktadır. Yöntemler, büyük veri setlerini analiz ederek otizm belirtilerini hızla tanımlamak, erken ve doğru teşhis sağlamak amacıyla bu çalışmada 17 girdi ve 1 hedef olmak üzere toplam 18 öznitelik değişkenden oluşan bir veri seti üzerinde öznitelik seçimi yöntemi ve sınıf dengeleme yöntemleri uygulanarak ardından dört farklı makine öğrenmesi algoritması K-En Yakın Komşu, Lojistik Regresyon, Naive Bayes, Destek Vektör Makineleri ile sınıflandırma işlemleri gerçekleştirilmiştir. Sınıflandırma performansı doğruluk, duyarlılık, özgüllük ve F1 skoru gibi metriklerle değerlendirilmiştir. Öznitelik seçimi sonrası, Destek Vektör Makineleri ve Lojistik Regresyon algoritmaları ile 100 doğruluk oranı elde edilirken, K-En Yakın Komşu ve Naive Bayes algoritmaları sırasıyla 94,7 ve 96,7 doğruluk sağlamıştır. Öznitelik seçimi yapılmadığında ise en yüksek doğruluk oranı 96,2 olarak kaydedilmiştir. Sonuçlar, öznitelik seçiminin makine öğrenmesi algoritmalarının sınıflandırma performansını belirgin bir şekilde artırdığını göstermektedir. Bu sonuçlar doğrultusunda OSB tanısında makine öğrenmesi yöntemlerinin uygulanabilirliğini ve doğruluğunu ortaya koymakta olup, teşhis sürecini iyileştirmek adına önemli bir katkı sağlamaktadır.

Makale detayları

Comparison of Different Classifiers Through Feature Selection in The Diagnosis of Autism Spectrum Disorder

Yazarlar:

Sinem Koç | Onur Sevli

Yayıncı:

Fırat University

Ana Başlıklar:

Makine Öğrenme (Diğer)

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.37 Issue 1, pp.61-74

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001334860

pISSN:

1308-9072

Machine Learning

Autism Spectrum Disorder

Autism Diagnosis

Feature selectionMakine Öğrenmesi

Otizm Spektrum Bozukluğu

Otizm Teşhisi

Öznitelik Seçimi

Sayı

Makale detayları

Yazarlar: Sinem Koç | Onur Sevli

Yayıncı: Fırat University

Ana Başlıklar: Makine Öğrenme (Diğer)

Dergi: Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.37 Issue 1, pp.61-74

Belge Tipi: Makale

Yayın Tarihi: 3/2025

Belge Dili: Türkçe

Document ID (Org No.): 2001334860

pISSN: 1308-9072

Sayı

Yazarlar:

Sinem Koç | Onur Sevli

Yayıncı:

Fırat University

Ana Başlıklar:

Makine Öğrenme (Diğer)

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.37 Issue 1, pp.61-74

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001334860

pISSN:

1308-9072