PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES

Ayşe Demirhan

doi:10.22531/muglajsci.422546

Research Article

MAKİNE ÖĞRENMESİ YÖNTEMLERİNİN OTİZM SPEKTRUM BOZUKLUĞU OLGULARININ BELİRLENMESİNDEKİ BAŞARIMI

Year 2018, Volume: 4 Issue: 1, 79 - 84, 27.06.2018

Ayşe Demirhan

https://doi.org/10.22531/muglajsci.422546

Cited By: 6

Abstract

Otizm spektrum bozukluğu (OSB) sosyal etkileşim ve iletişim
zayıflıkları şeklinde ortaya çıkan kalıtsal ve nörolojik bir gelişimsel
bozukluktur. OSB hastalığının teşhisi için klinik yöntemlerin yanında teşhis
süresini kısaltmak ve başarımı artırmak için makine öğrenmesi yöntemleri de
başarıyla uygulanmaktadır. Makine öğrenmesi yöntemleri yüksek boyutlu ve
çeşitli biyomedikal verilerin analizi için sundukları objektif algoritmalar ile
hastalıkların teşhisi konusunda yüksek performans göstermektedir. Makine
öğrenmesi yöntemleri, verilerdeki çok değişkenli ilişkileri yakaladığı ve bu
nedenle verilerdeki ince farkları tespit edebildiği için OSB gibi heterojen
durumlar içeren davranışsal bozuklukların tespit edilmesinde başarılı
olmaktadır. Bu çalışmada OSB ergen tarama verileri kullanılarak destek vektör
makineleri (DVM), k-en yakın komşu (kNN) ve rastgele orman (RO) makine
öğrenmesi yöntemleriyle OSB durumunun hızlı ve doğru olarak teşhis edilmesine
yönelik analizler yapılmış ve bu yöntemlerin performansları
karşılaştırılmıştır. DVM, kNN ve RO yöntemleri kullanılarak 10-kat çapraz
doğrulama ile yapılan ikili sınıflandırma işlemi sonucunda sırasıyla %95, %89
ve %100 doğruluk oranlarına erişilmiştir. Ayrıca, RO yöntemi ile yapılan
sınıflamadan % 100 duyarlılık ve belirlilik değerleri elde edilmiştir. Bu
çalışma ile OSB ergen tarama verilerini kullanarak RO yöntemi ile yapılan
sınıflama sonucunda OSB olgularının tam bir başarı ile tespit edilebildiği
gösterilmiştir.

Keywords

Otizm spektrum bozukluğu, makine öğrenmesi, destek vektör makineleri, k-en yakın komşu, rastgele orman

References

[1] Frith, U, Happé, F., “Autism spectrum disorder”, Current Biology, Vol. 15, No. 19, R786-R790, 2005.
[2] Charman, T., “Autism spectrum disorders”, Psychiatry, Vol. 7, No. 8, 331-334, 2008.
[3] Thabtah, F., “Machine learning in autistic spectrum disorder behavioral research: A review and ways forward”, Informatics for Health and Social Care, 1-20, 2018.
[4] Thabtah, F., “Autism spectrum disorder screening: machine learning adaptation and DSM-5 fulfillment”, Proceedings of the 1st International Conference on Medical and Health Informatics (ICMHI'17), Taichung City, Taiwan, 2017, 1-6.
[5] Duda, M., Ma, R., Haber, N., Wall, D. P., “Use of machine learning for behavioral distinction of autism and ADHD”, Translational Psychiatry, Vol. 6, No. 2, e732, 2016.
[6] Bone, D., Goodwin, M. S., Black, M. P., Lee, C. C., Audhkhasi, K., Narayanan, S., “Applying machine learning to facilitate autism diagnostics: pitfalls and promises”, Journal of Autism and Developmental Disorders, Vol. 45, No. 5, 1121-1136, 2015.
[7] Thabtah, F., ASDTests. A mobile app for ASD screening (Online). Available: www.asdtests.com [Accessed: 09.05.2018].
[8] Demirhan, A., “Nöro-görüntüleme tabanlı şizofreni teşhisi için desen analizi”, 25. IEEE Sinyal İşleme ve İletişim Uygulamaları (SİU 2017), Antalya, Turkey, 2017, 1-4.
[9] Demirhan, A., “Neuroimage-based clinical prediction using machine learning tools”, International Journal of Imaging Systems and Technology, Vol. 27, No. 1, 89-97, 2017.
[10] Vert, J. P., Tsuda, L., Schölkopf, B. (Editors, Schölkopf, B., Tsuda, L., Vert, J. P.), “A primer on kernel methods”, Kernel methods in computational biology, MIT Press, Cambridge, Massachusetts, 2004.
[11] Hashemian, M., Pourghassem, H., “Diagnosing autism spectrum disorders based on EEG analysis: A survey”, Neurophysiology, Vol. 46, No. 2, 183-195, 2014.
[12] Demirhan, A., “Random forests based recognition of the clinical labels using brain MRI scans”, 3rd International Conference on Frontiers of Signal Processing (ICFSP 2017), Paris, France, 2017, 156-159.
[13] Breiman, L., “Random forests”, Machine Learning, Vol. 45, No. 1, 5-32, 2001.
[14] Criminisi, A., Shotton, J., Konukoglu, E., “Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning”, Technical Report, Microsoft Research Lab - Cambridge, 2011.
[15] Hsu, C.-W., Chang, C.-C., Lin, C.-J., “A practical guide to support vector classification”, Technical Report, Department of Computer Science, National Taiwan University, 2003.
[16] Guttenberg, N., and Ryota, K., “Learning to generate classifiers”, arXiv preprint, arXiv:1803.11373, 2018.
[17] Basu, K., Autism Screening Adult Data Set: A Machine Approach, (Online). Available: https://github.com/kbasu2016/Autism-Detection-in-Adults/blob/master/report.pdf. [Accessed: 09.05.2018].

PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES

Year 2018, Volume: 4 Issue: 1, 79 - 84, 27.06.2018

Ayşe Demirhan

https://doi.org/10.22531/muglajsci.422546

Cited By: 6

Abstract

Autism spectrum disorder
(ASD) is an inherited and neurological developmental disorder characterized by
poor social interaction and communication weaknesses. In addition to the
clinical methods, machine learning methods have been successfully applied to shorten the duration of the
diagnosis and to increase the performance of the diagnosis of the ASD disease.
Machine learning methods demonstrate high performance in the diagnosis of
diseases with the objective algorithms they offer for the analysis of high-dimensional
and multimodal biomedical data. Machine learning methods are successful in
identifying the behavioral disorders such as OSB that include heterogeneous
conditions because they capture the multivariate relationships in the data and
therefore can detect subtle differences in data. In this study, analyzes are
performed for the fast and accurate diagnosis of the ASD status using support
vector machines (SVM), k-nearest neighbors (kNN) and random forest (RF) machine
learning methods using ASD adolescent scan data and the performance of these
methods are compared. Accuracy rates of 95%, 89%, and 100% are achieved as a result of binary
classification with 10-fold cross-validation (CV) using SVM, kNN, and RF
methods, respectively. Furthermore, 100% sensitivity and specificity values
were obtained from the classification with RF method. With this study, it has
been shown that ASD cases can be detected with complete success as a result of
classification with RF method using ASD adult screening data.

Keywords

Autism spectrum disorder, machine learning, support vector machine, k-nearest neighbor, random forest

References

[1] Frith, U, Happé, F., “Autism spectrum disorder”, Current Biology, Vol. 15, No. 19, R786-R790, 2005.
[2] Charman, T., “Autism spectrum disorders”, Psychiatry, Vol. 7, No. 8, 331-334, 2008.
[3] Thabtah, F., “Machine learning in autistic spectrum disorder behavioral research: A review and ways forward”, Informatics for Health and Social Care, 1-20, 2018.
[4] Thabtah, F., “Autism spectrum disorder screening: machine learning adaptation and DSM-5 fulfillment”, Proceedings of the 1st International Conference on Medical and Health Informatics (ICMHI'17), Taichung City, Taiwan, 2017, 1-6.
[5] Duda, M., Ma, R., Haber, N., Wall, D. P., “Use of machine learning for behavioral distinction of autism and ADHD”, Translational Psychiatry, Vol. 6, No. 2, e732, 2016.
[6] Bone, D., Goodwin, M. S., Black, M. P., Lee, C. C., Audhkhasi, K., Narayanan, S., “Applying machine learning to facilitate autism diagnostics: pitfalls and promises”, Journal of Autism and Developmental Disorders, Vol. 45, No. 5, 1121-1136, 2015.
[7] Thabtah, F., ASDTests. A mobile app for ASD screening (Online). Available: www.asdtests.com [Accessed: 09.05.2018].
[8] Demirhan, A., “Nöro-görüntüleme tabanlı şizofreni teşhisi için desen analizi”, 25. IEEE Sinyal İşleme ve İletişim Uygulamaları (SİU 2017), Antalya, Turkey, 2017, 1-4.
[9] Demirhan, A., “Neuroimage-based clinical prediction using machine learning tools”, International Journal of Imaging Systems and Technology, Vol. 27, No. 1, 89-97, 2017.
[10] Vert, J. P., Tsuda, L., Schölkopf, B. (Editors, Schölkopf, B., Tsuda, L., Vert, J. P.), “A primer on kernel methods”, Kernel methods in computational biology, MIT Press, Cambridge, Massachusetts, 2004.
[11] Hashemian, M., Pourghassem, H., “Diagnosing autism spectrum disorders based on EEG analysis: A survey”, Neurophysiology, Vol. 46, No. 2, 183-195, 2014.
[12] Demirhan, A., “Random forests based recognition of the clinical labels using brain MRI scans”, 3rd International Conference on Frontiers of Signal Processing (ICFSP 2017), Paris, France, 2017, 156-159.
[13] Breiman, L., “Random forests”, Machine Learning, Vol. 45, No. 1, 5-32, 2001.
[14] Criminisi, A., Shotton, J., Konukoglu, E., “Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning”, Technical Report, Microsoft Research Lab - Cambridge, 2011.
[15] Hsu, C.-W., Chang, C.-C., Lin, C.-J., “A practical guide to support vector classification”, Technical Report, Department of Computer Science, National Taiwan University, 2003.
[16] Guttenberg, N., and Ryota, K., “Learning to generate classifiers”, arXiv preprint, arXiv:1803.11373, 2018.
[17] Basu, K., Autism Screening Adult Data Set: A Machine Approach, (Online). Available: https://github.com/kbasu2016/Autism-Detection-in-Adults/blob/master/report.pdf. [Accessed: 09.05.2018].

There are 17 citations in total.

Details

Primary Language	English
Subjects	Engineering
Journal Section	Journals
Authors	Ayşe Demirhan 0000-0001-9227-9210
Publication Date	June 27, 2018
Published in Issue	Year 2018 Volume: 4 Issue: 1

Cite

APA	Demirhan, A. (2018). PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES. Mugla Journal of Science and Technology, 4(1), 79-84. https://doi.org/10.22531/muglajsci.422546
AMA	Demirhan A. PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES. Mugla Journal of Science and Technology. June 2018;4(1):79-84. doi:10.22531/muglajsci.422546
Chicago	Demirhan, Ayşe. “PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES”. Mugla Journal of Science and Technology 4, no. 1 (June 2018): 79-84. https://doi.org/10.22531/muglajsci.422546.
EndNote	Demirhan A (June 1, 2018) PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES. Mugla Journal of Science and Technology 4 1 79–84.
IEEE	A. Demirhan, “PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES”, Mugla Journal of Science and Technology, vol. 4, no. 1, pp. 79–84, 2018, doi: 10.22531/muglajsci.422546.
ISNAD	Demirhan, Ayşe. “PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES”. Mugla Journal of Science and Technology 4/1 (June 2018), 79-84. https://doi.org/10.22531/muglajsci.422546.
JAMA	Demirhan A. PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES. Mugla Journal of Science and Technology. 2018;4:79–84.
MLA	Demirhan, Ayşe. “PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES”. Mugla Journal of Science and Technology, vol. 4, no. 1, 2018, pp. 79-84, doi:10.22531/muglajsci.422546.
Vancouver	Demirhan A. PERFORMANCE OF MACHINE LEARNING METHODS IN DETERMINING THE AUTISM SPECTRUM DISORDER CASES. Mugla Journal of Science and Technology. 2018;4(1):79-84.