Araştırma Makalesi
BibTex RIS Kaynak Göster

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

Yıl 2019, Cilt: 12 Sayı: 1, 33 - 41, 31.01.2019
https://doi.org/10.17671/gazibtd.458102

Öz

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.

Kaynakça

  • [1] American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders (DSM 5), Washington, DC, American Psychiatric Association, 2013.
  • [2] C. K. Whalen, B. Henker, L. D. Jamner, S. S. Ishikawa, J. N. Floro, R. Swindle, A. R. Perwien, J. A. Johnston, “Toward mapping daily challenges of living with ADHD: Maternal and child perspectives using electronic diaries”, Journal of Abnormal Child Psychology, 34(1), 111-126, 2006.
  • [3] M. Weiss, G. Weiss, Attention Deficit Hyperactivity Disorder. Child and Adolescent Psychiatry, A Comprehensive Textbook, Lewis M. (editor), Philadelphia: Lippincott William and Wilkins, 647–650, 2002.
  • [4] R. A. Barkley, Attention Deficit Hyperactivity Disorder. Child Psychopathology, E. J. Mash, R. A. Barkley (editor), New York: Guilford Publications, 63-112. 1996.
  • [5] J. O. Larsson, H. Larsson, P. Lichtenstein, “Genetic and environmental contributions to stability and change of ADHD symptoms between 8 and 13 years of age: a longitudinal twin study”, Journal of the American Academy of Child & Adolescent Psychiatry, 43(10), 1267-1275, 2004.
  • [6] S. Pliszka, AACAP Work Group on Quality Issues, “Practice parameter for the assessment and treatment of children and adolescents with attention-deficit/hyperactivity disorder”, Journal of the American Academy of Child & Adolescent Psychiatry, 46(7), 894-921, 2007.
  • [7] M. S. Bhatia, V. R. Nigam, N. Bohra, S. C. Malik, “Attention deficit with hyperactivity disorder among pediatric outpatients”, Journal of Child Psychology and Psychiatry, 33(2), 297-306. 1991.
  • [8] C. Tuğlu, O. O. Şahin, “Adult attention deficit hyperactivity disorder: neurobiology, diagnostic problems and clinical features”, Current Approaches in Psychiatry, 2(1), 75-116, 2010.
  • [9] B. Öncü, S. Şenol, “The etiology of attention deficit hyperactivity disorder: An integrative approach”, Journal of Clinical Psychiatry, 5(1), 111-119, 2002.
  • [10] T. R. Insel, “The NIMH research domain criteria (RDoC) project: precision medicine for psychiatry”, American Journal of Psychiatry, 171(4), 395-397, 2014.
  • [11] F. Seixasa, B. Zadroznyb, J. Laksc, A. Conci, D. C. M. Saadea, “A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer’s disease and mild cognitive impairment”, Computers in Biology and Medicine, 51, 140–158, 2014.
  • [12] H. Göker, İ. Şahin, H. Tekedere, “Erken çocukluk döneminde otizm teşhisine yönelik dinamik uzman sistem tasarımı”, Bilişim Teknolojileri Dergisi, 8(3), 167, 2015.
  • [13] X. Zhanga, B. Hub, X. Maa, P. Moorec, J. Chena, “Ontology driven decision support for the diagnosis of mild cognitive impairment”, Computers in Biology and Medicine, 113, 781–791, 2014.
  • [14] L. C. Nunes, P. R. Pinheiro, T. C. Pequeno, “An expert system applied to the diagnosis of psychological disorders”, In Intelligent Computing and Intelligent Systems, IEEE International Conference on IEEE, 363-367, November, 2009.
  • [15] A. P. Cha, A. Romli, “Human-computer interaction of design rules and usability elements in expert system for personality-based stress management”, International Journal of Intelligent Computing Research (IJICR), 1(1/2), 33-42, 2010.
  • [16] S. R. Manalu, B. S. Abbas, F. L. Gaol, B. Trawiński, “An expert system to assist with early detection of schizophrenia”. In Asian Conference on Intelligent Information and Database Systems, 802-812, 2017.
  • [17] J. M. De la Fuente, E. Bengoetxea, F. Navarro, J. Bobes, R. D. Alarcón, “Interconnection between biological abnormalities in borderline personality disorder: use of the bayesian networks model”, Psychiatry Research, 186(2), 315-319, 2011.
  • [18] C. Amrit, T. Paauw, R. Aly, M. Lavric, “Identifying child abuse through text mining and machine learning”, Expert Systems with Applications, 88(1), 402-418, 2017.
  • [19] R. B. Ramoni, N. L. Saccone, D. K. Hatsukami, L. J. Bierut, M. F. Ramoni, “A testable prognostic model of nicotine dependence”, Journal of Neurogenetics, 23(3), 283–92, 2009.
  • [20] K. R. Hole, V. S. Gulhane, “Rule-based expert system for the diagnosis of memory loss diseases”, International Journal of Innovative Science, Engineering & Technology, 1(3).80- 83, 2014.
  • [21] S. M. Fakhrahmad, M. H. Sadreddini, M. J. Zolghadri, “A proposed expert system for word sense disambiguation: Deductive ambiguity resolution based on data mining and forward chaining”, Expert Systems, 32(2), 178-191, 2015.
  • [22] Oktoria, C. H. Yang, L. Y. Chuang, “An Application of expert system for diagnosing fever caused by viral infection”, Journal of Life Sciences and Technologies, 4(1), 17-21, 2016.
  • [23] S. Kamley, S. Jaloree, R. S. Thakur, “Performance comparison between forward and backward chaining rule based expert system approaches over global stock exchanges”, International Journal of Computer Science and Information Security, 14(3), 74, 2016.
  • [24] O. Matthew, K. Buckley, M. Garvey, R. Moreton, “Multi-tenant database framework validation and implementation into an expert system”, International Journal of Advanced Studies in Computers, Science and Engineering, 5(8), 13-21, 2016.
  • [25] A. Jadhav, A. Pandita, A. Pawar, V. Singh, “Classification of unstructured data using naïve bayes classifier and predictive analysis for RTI application”, An International Journal of Engineering & Technology, 3(6), 1-6, 2016.
  • [26] S. S. Nikam, “A comparative study of classification techniques in data mining algorithms”, Oriental Journal of Computer Science & Technology, 8(1), 13-19, April, 2015.
  • [27] A. Choi, N. Tavabi, A. Darwiche, “Structured features in naive bayes classification”, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 3233-3240, February, 2016.
  • [28] K. Wang, W. Shang, “Outcome prediction of DOTA2 based on naïve bayes classifier”, In Computer and Information Science (ICIS) IEEE/ACIS 16th International Conference on IEEE, 591-593, May, 2017.
  • [29] H. Akpınar, “Knowledge discovery in databases and data mining”, Istanbul University Journal of the School of Business, 29(1) 1-22, 2000.
  • [30] J. Davis, M. Goadrich, “The relationship between Precision-Recall and ROC curves”, In Proceedings of the 23rd International Conference on Machine Learning, 233-240, June, 2006.
  • [31] T. Fawcett, “An introduction to ROC analysis”, Pattern Recognition Letters, 27(8), 861-874, 2006.
  • [32] A. C. Tantuğ, “Text classification”. TBV Journal of Computer Science and Engineering, 5(2), 1-12, 2012.
  • [33] N. Allahverdi, Uzman Sistemler: Bir Yapay Zeka Uygulaması, İstanbul: Atlas Yayıncılık, 16-20, 2002.
  • [34] D. L. Xu, J. Liu, J. B. Yang, G. P. Liu, J. Wang, I. Jenkinson, J. Ren, “Inference and learning methodology of belief-rule-based expert system for pipeline leak detection”, Expert Systems with Applications, 32(1), 103– 113, 2007.
  • [35] J. B. Yang, J. Liu, D. L. Xu, J. Wang, H. W. Wang, “Optimal learning method for training belief rule-based systems”, IEEE Transactions on Systems, Man, and Cybernetics (Part A), 37, 569-585, 2007.
  • [36] M. S. Hossain, S. Rahaman, R. Mustafa, K. Andersson, “A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty”, Soft Computing, 1-16, 2017.
  • [37] N. Hassan, N. Arbaiy, N. A. A. Shah, Z. A. Afif, “Fuzzy expert system for heart attack diagnosis”, In IOP Conference Series: Materials Science and Engineering, 226(1), 012111, August, 2017.
  • [38] M. Erkalan, M. H. Calp, İ. Şahin, “Çoklu zekâ kuramından yararlanılarak meslek seçiminde kullanılacak bir uzman sistem tasarımı ve gerçekleştirilmesi”, Bilişim Teknolojileri Dergisi, 5 (2), 49-55, 2012.
  • [39] N. T. Mahmood, “Estimation medicine for diseases system to support medical diagnosis by expert system”, International Journal of Advanced Computer Science and Applications, 7(9), 140-144, 2016.
  • [40] E. Caballero-Ruiz, G. García-Sáez, M. Rigla, M. Villaplana, B. Pons, M. E. Hernando, “Automatic classification of glycaemia measurements to enhance data interpretation in an expert system for gestational diabetes”, Expert Systems with Applications, 63, 386-396, 2016.
  • [41] F. Khozeimeh, R. Alizadehsani, M. Roshanzamir, A. Khosravi, P. Layegh, S. Nahavandi, “An expert system for selecting wart treatment method”, Computers in Biology and Medicine, 81, 167-175, 2017.
  • [42] B. Alić, L. Gurbeta, A. Badnjević, A. Badnjević-Čengić, M. Malenica, T. Dujić, A. Čaušević, T. Bego, “Classification of metabolic syndrome patients using implemented expert system”, In CMBEBIH IFMBE Proceedings, Springer, Singapore, 62, 601-607, 2017.
  • [43] J. Vila-Francés, J. Sanchís, E. Soria-Olivas, A. J. Serrano, M. Martínez-Sober, C. Bonanad, S. Ventura, “Expert system for predicting unstable angina based on Bayesian networks”, Expert Systems with Applications, 40(12), 5004-5010, 2013.
  • [44] İ. Şahin, M. H. Calp, A. Özkan, “An Expert System Design and Application for Hydroponics Greenhouse System”, Ankara Turkey. Gazi University Journal of Science, vol. 27, no. 2, pp. 809-822, 2014.
  • [45] İ. Şahin, M. H. Calp, Ö. Akça, “Kredibilite Notu Değerlendirmeye Yönelik Bir Uzman Sistem Yaklaşımı”, Politeknik Dergisi, Cilt:14 Sayı: 1, s. 79-83, 2011.
  • [46] E. V. Popov, I. B. Fominykh, E. B. Kisel, M. D. Shapot, Statistical and Dynamic Expert Systems. Moscow: Finance and Statistics, 139, 1996.
  • [47] Y. Kaya, R. Tekin, “Epileptik nöbetlerin tespiti için aşırı öğrenme makinesi tabanlı uzman bir sistem”, Bilişim Teknolojileri Dergisi, 5(2), 33-40, 2012.
  • [48] J. Sigut, J. Piñeiro, E. Gonzalez, J. Torres, “An expert system for supervised classifier design: Application to Alzheimer diagnosis”, Expert Systems with Applications, 32(3), 927-938, 2007.
  • [49] P. K. Singh, R. Sarkar, “A simple and effective expert system for schizophrenia detection”, International Journal of Intelligent Systems Technologies and Applications, 14(1), 27-49, 2015.
  • [50] T. Paiva, T. Penzel, “An Expert system for the diagnosis of sleep disorders”, European Neurological Network: ENN, 78, 127, 2000.
  • [51] A. Yıldız, M. Akın, M. Poyraz, “An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings”, Expert Systems with Applications, 38(10), 12880-12890, 2011.
  • [52] U. Çelik, N. Yurtay, “An ant colony optimization algorithm-based classification for the diagnosis of primary headaches using a website questionnaire expert system”, Turkish Journal of Electrical Engineering & Computer Sciences, 25(5), 4200-4210, 2017.
  • [53] M. Ochab, W. Wajs, “Expert system supporting an early prediction of the bronchopulmonary dysplasia”, Computers in Biology and Medicine, 69, 236-244, 2016.
  • [54] E. Avcı, “A new expert system for diagnosis of lung cancer: GDA—LS_SVM”, Journal of Medical Systems, 36(3), 2005-2009, 2012.
  • [55] A. Keleş, A. Keleş, “Extracting fuzzy rules for the diagnosis of breast cancer”, Turkish Journal of Electrical Engineering & Computer Sciences, 21(5), 1495-1503, 2013.
  • [56] A. T. Azar, A. E. Hassanien, T. H. Kim, “Expert system based on neural-fuzzy rules for thyroid diseases diagnosis”, In Computer Applications for Bio-technology, Multimedia and Ubiquitous City, 94-105, 2012.
  • [57] X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, P. S. Yu, Z. Zhou, M. Steinbach, D. J. Hand, D. Steinberg, “Top 10 algorithms in data mining”, Knowledge and Information Systems, 14(1), 1-37, 2008.
  • [58] H. Göker, H. I. Bülbül, E. Irmak, “The estimation of students' academic success by data mining methods”, In Machine Learning and Applications (ICMLA), 2013 12th International Conference on IEEE, 2, 535-539, December, 2013.
  • [59] J. R. Landis, G. G. Koch, “The measurement of observer agreement for categorical data”, Biometrics, 33, 159-174, 1977.
  • [60] F. Ghassemi, M. Hassan-Moradi, M. Tehrani-Doost, V. Abootalebi, “Using non-linear features of EEG for ADHD/normal participants’ classification”, Procedia-Social and Behavioral Sciences, 32, 148-152, 2012.
  • [61] M. N. I. Qureshi, J. Oh, B. Min, H. J. Jo, B. Lee, “Multi-modal, multi-measure, and multi-class discrimination of ADHD with hierarchical feature extraction and extreme learning machine using structural and functional brain MRI”, Frontiers in Human Neuroscience, 11, 157, 2017.
  • [62] A. Tenev, S. Markovska-Simoska, L. Kocarev, J. Pop-Jordanov, A. Müller, G. Candrian, “Machine learning approach for classification of ADHD adults”, International Journal of Psychophysiology, 93(1), 162-166, 2014.
  • [63] M. R. Mohammadi, A. Khaleghi, A. M. Nasrabadi, S. Rafieivand, M. Begol, H. Zarafshan, “EEG classification of ADHD and normal children using non-linear features and neural network”, Biomedical Engineering Letters, 6(2), 66-73, 2016.
  • [64] X. Peng, P. Lin, T. Zhang, J. Wang, “Extreme learning machine-based classification of ADHD using brain structural MRI data”, PLOS One, 8(11), 1-12, 2013.

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

Yıl 2019, Cilt: 12 Sayı: 1, 33 - 41, 31.01.2019
https://doi.org/10.17671/gazibtd.458102

Öz

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. 

Kaynakça

  • [1] American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders (DSM 5), Washington, DC, American Psychiatric Association, 2013.
  • [2] C. K. Whalen, B. Henker, L. D. Jamner, S. S. Ishikawa, J. N. Floro, R. Swindle, A. R. Perwien, J. A. Johnston, “Toward mapping daily challenges of living with ADHD: Maternal and child perspectives using electronic diaries”, Journal of Abnormal Child Psychology, 34(1), 111-126, 2006.
  • [3] M. Weiss, G. Weiss, Attention Deficit Hyperactivity Disorder. Child and Adolescent Psychiatry, A Comprehensive Textbook, Lewis M. (editor), Philadelphia: Lippincott William and Wilkins, 647–650, 2002.
  • [4] R. A. Barkley, Attention Deficit Hyperactivity Disorder. Child Psychopathology, E. J. Mash, R. A. Barkley (editor), New York: Guilford Publications, 63-112. 1996.
  • [5] J. O. Larsson, H. Larsson, P. Lichtenstein, “Genetic and environmental contributions to stability and change of ADHD symptoms between 8 and 13 years of age: a longitudinal twin study”, Journal of the American Academy of Child & Adolescent Psychiatry, 43(10), 1267-1275, 2004.
  • [6] S. Pliszka, AACAP Work Group on Quality Issues, “Practice parameter for the assessment and treatment of children and adolescents with attention-deficit/hyperactivity disorder”, Journal of the American Academy of Child & Adolescent Psychiatry, 46(7), 894-921, 2007.
  • [7] M. S. Bhatia, V. R. Nigam, N. Bohra, S. C. Malik, “Attention deficit with hyperactivity disorder among pediatric outpatients”, Journal of Child Psychology and Psychiatry, 33(2), 297-306. 1991.
  • [8] C. Tuğlu, O. O. Şahin, “Adult attention deficit hyperactivity disorder: neurobiology, diagnostic problems and clinical features”, Current Approaches in Psychiatry, 2(1), 75-116, 2010.
  • [9] B. Öncü, S. Şenol, “The etiology of attention deficit hyperactivity disorder: An integrative approach”, Journal of Clinical Psychiatry, 5(1), 111-119, 2002.
  • [10] T. R. Insel, “The NIMH research domain criteria (RDoC) project: precision medicine for psychiatry”, American Journal of Psychiatry, 171(4), 395-397, 2014.
  • [11] F. Seixasa, B. Zadroznyb, J. Laksc, A. Conci, D. C. M. Saadea, “A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer’s disease and mild cognitive impairment”, Computers in Biology and Medicine, 51, 140–158, 2014.
  • [12] H. Göker, İ. Şahin, H. Tekedere, “Erken çocukluk döneminde otizm teşhisine yönelik dinamik uzman sistem tasarımı”, Bilişim Teknolojileri Dergisi, 8(3), 167, 2015.
  • [13] X. Zhanga, B. Hub, X. Maa, P. Moorec, J. Chena, “Ontology driven decision support for the diagnosis of mild cognitive impairment”, Computers in Biology and Medicine, 113, 781–791, 2014.
  • [14] L. C. Nunes, P. R. Pinheiro, T. C. Pequeno, “An expert system applied to the diagnosis of psychological disorders”, In Intelligent Computing and Intelligent Systems, IEEE International Conference on IEEE, 363-367, November, 2009.
  • [15] A. P. Cha, A. Romli, “Human-computer interaction of design rules and usability elements in expert system for personality-based stress management”, International Journal of Intelligent Computing Research (IJICR), 1(1/2), 33-42, 2010.
  • [16] S. R. Manalu, B. S. Abbas, F. L. Gaol, B. Trawiński, “An expert system to assist with early detection of schizophrenia”. In Asian Conference on Intelligent Information and Database Systems, 802-812, 2017.
  • [17] J. M. De la Fuente, E. Bengoetxea, F. Navarro, J. Bobes, R. D. Alarcón, “Interconnection between biological abnormalities in borderline personality disorder: use of the bayesian networks model”, Psychiatry Research, 186(2), 315-319, 2011.
  • [18] C. Amrit, T. Paauw, R. Aly, M. Lavric, “Identifying child abuse through text mining and machine learning”, Expert Systems with Applications, 88(1), 402-418, 2017.
  • [19] R. B. Ramoni, N. L. Saccone, D. K. Hatsukami, L. J. Bierut, M. F. Ramoni, “A testable prognostic model of nicotine dependence”, Journal of Neurogenetics, 23(3), 283–92, 2009.
  • [20] K. R. Hole, V. S. Gulhane, “Rule-based expert system for the diagnosis of memory loss diseases”, International Journal of Innovative Science, Engineering & Technology, 1(3).80- 83, 2014.
  • [21] S. M. Fakhrahmad, M. H. Sadreddini, M. J. Zolghadri, “A proposed expert system for word sense disambiguation: Deductive ambiguity resolution based on data mining and forward chaining”, Expert Systems, 32(2), 178-191, 2015.
  • [22] Oktoria, C. H. Yang, L. Y. Chuang, “An Application of expert system for diagnosing fever caused by viral infection”, Journal of Life Sciences and Technologies, 4(1), 17-21, 2016.
  • [23] S. Kamley, S. Jaloree, R. S. Thakur, “Performance comparison between forward and backward chaining rule based expert system approaches over global stock exchanges”, International Journal of Computer Science and Information Security, 14(3), 74, 2016.
  • [24] O. Matthew, K. Buckley, M. Garvey, R. Moreton, “Multi-tenant database framework validation and implementation into an expert system”, International Journal of Advanced Studies in Computers, Science and Engineering, 5(8), 13-21, 2016.
  • [25] A. Jadhav, A. Pandita, A. Pawar, V. Singh, “Classification of unstructured data using naïve bayes classifier and predictive analysis for RTI application”, An International Journal of Engineering & Technology, 3(6), 1-6, 2016.
  • [26] S. S. Nikam, “A comparative study of classification techniques in data mining algorithms”, Oriental Journal of Computer Science & Technology, 8(1), 13-19, April, 2015.
  • [27] A. Choi, N. Tavabi, A. Darwiche, “Structured features in naive bayes classification”, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 3233-3240, February, 2016.
  • [28] K. Wang, W. Shang, “Outcome prediction of DOTA2 based on naïve bayes classifier”, In Computer and Information Science (ICIS) IEEE/ACIS 16th International Conference on IEEE, 591-593, May, 2017.
  • [29] H. Akpınar, “Knowledge discovery in databases and data mining”, Istanbul University Journal of the School of Business, 29(1) 1-22, 2000.
  • [30] J. Davis, M. Goadrich, “The relationship between Precision-Recall and ROC curves”, In Proceedings of the 23rd International Conference on Machine Learning, 233-240, June, 2006.
  • [31] T. Fawcett, “An introduction to ROC analysis”, Pattern Recognition Letters, 27(8), 861-874, 2006.
  • [32] A. C. Tantuğ, “Text classification”. TBV Journal of Computer Science and Engineering, 5(2), 1-12, 2012.
  • [33] N. Allahverdi, Uzman Sistemler: Bir Yapay Zeka Uygulaması, İstanbul: Atlas Yayıncılık, 16-20, 2002.
  • [34] D. L. Xu, J. Liu, J. B. Yang, G. P. Liu, J. Wang, I. Jenkinson, J. Ren, “Inference and learning methodology of belief-rule-based expert system for pipeline leak detection”, Expert Systems with Applications, 32(1), 103– 113, 2007.
  • [35] J. B. Yang, J. Liu, D. L. Xu, J. Wang, H. W. Wang, “Optimal learning method for training belief rule-based systems”, IEEE Transactions on Systems, Man, and Cybernetics (Part A), 37, 569-585, 2007.
  • [36] M. S. Hossain, S. Rahaman, R. Mustafa, K. Andersson, “A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty”, Soft Computing, 1-16, 2017.
  • [37] N. Hassan, N. Arbaiy, N. A. A. Shah, Z. A. Afif, “Fuzzy expert system for heart attack diagnosis”, In IOP Conference Series: Materials Science and Engineering, 226(1), 012111, August, 2017.
  • [38] M. Erkalan, M. H. Calp, İ. Şahin, “Çoklu zekâ kuramından yararlanılarak meslek seçiminde kullanılacak bir uzman sistem tasarımı ve gerçekleştirilmesi”, Bilişim Teknolojileri Dergisi, 5 (2), 49-55, 2012.
  • [39] N. T. Mahmood, “Estimation medicine for diseases system to support medical diagnosis by expert system”, International Journal of Advanced Computer Science and Applications, 7(9), 140-144, 2016.
  • [40] E. Caballero-Ruiz, G. García-Sáez, M. Rigla, M. Villaplana, B. Pons, M. E. Hernando, “Automatic classification of glycaemia measurements to enhance data interpretation in an expert system for gestational diabetes”, Expert Systems with Applications, 63, 386-396, 2016.
  • [41] F. Khozeimeh, R. Alizadehsani, M. Roshanzamir, A. Khosravi, P. Layegh, S. Nahavandi, “An expert system for selecting wart treatment method”, Computers in Biology and Medicine, 81, 167-175, 2017.
  • [42] B. Alić, L. Gurbeta, A. Badnjević, A. Badnjević-Čengić, M. Malenica, T. Dujić, A. Čaušević, T. Bego, “Classification of metabolic syndrome patients using implemented expert system”, In CMBEBIH IFMBE Proceedings, Springer, Singapore, 62, 601-607, 2017.
  • [43] J. Vila-Francés, J. Sanchís, E. Soria-Olivas, A. J. Serrano, M. Martínez-Sober, C. Bonanad, S. Ventura, “Expert system for predicting unstable angina based on Bayesian networks”, Expert Systems with Applications, 40(12), 5004-5010, 2013.
  • [44] İ. Şahin, M. H. Calp, A. Özkan, “An Expert System Design and Application for Hydroponics Greenhouse System”, Ankara Turkey. Gazi University Journal of Science, vol. 27, no. 2, pp. 809-822, 2014.
  • [45] İ. Şahin, M. H. Calp, Ö. Akça, “Kredibilite Notu Değerlendirmeye Yönelik Bir Uzman Sistem Yaklaşımı”, Politeknik Dergisi, Cilt:14 Sayı: 1, s. 79-83, 2011.
  • [46] E. V. Popov, I. B. Fominykh, E. B. Kisel, M. D. Shapot, Statistical and Dynamic Expert Systems. Moscow: Finance and Statistics, 139, 1996.
  • [47] Y. Kaya, R. Tekin, “Epileptik nöbetlerin tespiti için aşırı öğrenme makinesi tabanlı uzman bir sistem”, Bilişim Teknolojileri Dergisi, 5(2), 33-40, 2012.
  • [48] J. Sigut, J. Piñeiro, E. Gonzalez, J. Torres, “An expert system for supervised classifier design: Application to Alzheimer diagnosis”, Expert Systems with Applications, 32(3), 927-938, 2007.
  • [49] P. K. Singh, R. Sarkar, “A simple and effective expert system for schizophrenia detection”, International Journal of Intelligent Systems Technologies and Applications, 14(1), 27-49, 2015.
  • [50] T. Paiva, T. Penzel, “An Expert system for the diagnosis of sleep disorders”, European Neurological Network: ENN, 78, 127, 2000.
  • [51] A. Yıldız, M. Akın, M. Poyraz, “An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings”, Expert Systems with Applications, 38(10), 12880-12890, 2011.
  • [52] U. Çelik, N. Yurtay, “An ant colony optimization algorithm-based classification for the diagnosis of primary headaches using a website questionnaire expert system”, Turkish Journal of Electrical Engineering & Computer Sciences, 25(5), 4200-4210, 2017.
  • [53] M. Ochab, W. Wajs, “Expert system supporting an early prediction of the bronchopulmonary dysplasia”, Computers in Biology and Medicine, 69, 236-244, 2016.
  • [54] E. Avcı, “A new expert system for diagnosis of lung cancer: GDA—LS_SVM”, Journal of Medical Systems, 36(3), 2005-2009, 2012.
  • [55] A. Keleş, A. Keleş, “Extracting fuzzy rules for the diagnosis of breast cancer”, Turkish Journal of Electrical Engineering & Computer Sciences, 21(5), 1495-1503, 2013.
  • [56] A. T. Azar, A. E. Hassanien, T. H. Kim, “Expert system based on neural-fuzzy rules for thyroid diseases diagnosis”, In Computer Applications for Bio-technology, Multimedia and Ubiquitous City, 94-105, 2012.
  • [57] X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, P. S. Yu, Z. Zhou, M. Steinbach, D. J. Hand, D. Steinberg, “Top 10 algorithms in data mining”, Knowledge and Information Systems, 14(1), 1-37, 2008.
  • [58] H. Göker, H. I. Bülbül, E. Irmak, “The estimation of students' academic success by data mining methods”, In Machine Learning and Applications (ICMLA), 2013 12th International Conference on IEEE, 2, 535-539, December, 2013.
  • [59] J. R. Landis, G. G. Koch, “The measurement of observer agreement for categorical data”, Biometrics, 33, 159-174, 1977.
  • [60] F. Ghassemi, M. Hassan-Moradi, M. Tehrani-Doost, V. Abootalebi, “Using non-linear features of EEG for ADHD/normal participants’ classification”, Procedia-Social and Behavioral Sciences, 32, 148-152, 2012.
  • [61] M. N. I. Qureshi, J. Oh, B. Min, H. J. Jo, B. Lee, “Multi-modal, multi-measure, and multi-class discrimination of ADHD with hierarchical feature extraction and extreme learning machine using structural and functional brain MRI”, Frontiers in Human Neuroscience, 11, 157, 2017.
  • [62] A. Tenev, S. Markovska-Simoska, L. Kocarev, J. Pop-Jordanov, A. Müller, G. Candrian, “Machine learning approach for classification of ADHD adults”, International Journal of Psychophysiology, 93(1), 162-166, 2014.
  • [63] M. R. Mohammadi, A. Khaleghi, A. M. Nasrabadi, S. Rafieivand, M. Begol, H. Zarafshan, “EEG classification of ADHD and normal children using non-linear features and neural network”, Biomedical Engineering Letters, 6(2), 66-73, 2016.
  • [64] X. Peng, P. Lin, T. Zhang, J. Wang, “Extreme learning machine-based classification of ADHD using brain structural MRI data”, PLOS One, 8(11), 1-12, 2013.
Toplam 64 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Hanife Göker

Hakan Tekedere

Yayımlanma Tarihi 31 Ocak 2019
Gönderilme Tarihi 7 Eylül 2018
Yayımlandığı Sayı Yıl 2019 Cilt: 12 Sayı: 1

Kaynak Göster

APA Göker, H., & Tekedere, H. (2019). Dynamic Expert System Design for the Prediction of Attention Deficit and Hyperactivity Disorder in Childhood. Bilişim Teknolojileri Dergisi, 12(1), 33-41. https://doi.org/10.17671/gazibtd.458102