Yıl 2020, Cilt 8 , Sayı 1, Sayfalar 175 - 180 2020-01-28

Importance of Attribute Selection for Parkinson Disease
Parkinson Hastalığı İçin Öznitelik Seçiminin Önemi

Kemal AKYOL [1] , Şafak BAYIR [2] , Baha ŞEN [3]


Parkinson disease is a neurological disorder occurring at older ages. It is one of the most painful, dangerous and untreated diseases. In this study, a new application based on assessing the importance of attributes using the ranking techniques was carried out for diagnosis of this disease. The effects of the attributes on the Parkinson disease are determined by utilizing Stability Selection method. The selected attributes dataset and all attributes dataset have been sent as input data to the Random Forest and Logistic Regression algorithms in order to investigate the best model which is to be effective in the diagnosis of this disease. This study including the model which presented the best performance might be a powerful tool for effective diagnosis of this disease.

Parkinson hastalığı, ileri yaşlarda ortaya çıkan nörolojik bir hastalıktır. Bu hastalık, en acı verici, tehlikeli ve tedavi edilmeyen hastalıklardan biridir. Bu çalışmada, bu hastalığın tespiti için sıralama tekniklerini kullanarak özniteliklerin önemliliğini değerlendirmeye dayalı yeni bir uygulama yapılmıştır. Parkinson hastalığı üzerinde özniteliklerin etkilerinin tanımlanması için Stability Selection (Kararlılık Seçimi) metodu uygulanmıştır. Bu hastalığın tanısında etkin olacak olan en iyi modeli elde etmek için seçilmiş öznitelikler veriseti ve tüm öznitelikler veriseti giriş verisi olarak Rastgele Orman ve Lojistik Regresyon algoritmalarına gönderilmiştir. En iyi performans sunan model bilgisini içeren bu çalışma bu hastalığın etkin teşhisi için güçlü bir araç olabilir.
  • [1] G. Yadav, Y. Kumar, G. Sahoo, “Predication of Parkinson's disease using data mining methods: a comparative analysis of tree, statistical, and support vector machine classifiers”, Indian J Med Sci vol. 65, no. 6, pp. 231-242, 2011.
  • [2] K. Al-Tawil, A. Akrami, H. Youssef, “A new authentication protocol for GSM networks”, In: Proceedings of the 23rd Annual Conference on Local Computer Networks, 11-14 Oct 1998, Lowell, MA, USA, 1998.
  • [3] R. Subrata and A. Zomaya, “Artificial Life Techniques for Reporting Cell Planning in Mobile Computing”, In: Proceedings of the International Parallel and Distributed Processing Symposium vol. 14, pp. 169–187, 2003.
  • [4] M.A. Little, P.E. McSharry, E.J. Hunter, J. Spielman, L.O. Ramig, “Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease”, IEEE T Bio-Med Eng vol. 56, no. 4, pp. 1015-1022, 2009.
  • [5] R. Das, “A comparison of multiple classification methods for diagnosis of Parkinson disease”, Expert Syst Appl vol. 37, no. 2, pp. 1568-1572, 2010.
  • [6] R.A. Barker and S.B. Dunnett, “Functional integration of neural grafts in Parkinson’s disease”, Nat Neurosci vol. 2, no.12, pp. 1047-1048, 1999.
  • [7] L.O. Ramig, C. Fox, S. Sapir, “Parkinson's disease: Speech and voice disorders and their treatment with the Lee Silverman Voice Treatment”, Seminars in Speech and Language vol. 25, no. 2, pp. 169-180, 2004.
  • [8] A.K. Ho, R. Iansek, C. Marigliani, J.L. Bradshaw, S. Gates, “Speech impairment in a large sample of patients with Parkinson’s disease”, Behav Neurol vol. 11, no. 3, pp. 131-137, 1998.
  • [9] K. Umapathy, S. Krishnan, V. Parsa, D.G. Jamieson, “Discrimination of pathological Voices Using a Time-Frequency Approach”, IEEE T Bio-Med Eng vol. 52, no. 3, pp. 421-430, 2005.
  • [10] J.I. Godino Lorente and P. Gomez-Vilda, “Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors”, IEEE T Bio-Med Eng vol. 51, no. 2, pp. 380-384, 2004.
  • [11] R. Shrivastav, “The use of an auditory model in predicting perceptual ratings of breathy voice quality”, J Voice vol. 17, no. 4, pp. 502-512. 2003.
  • [12] B.E. Sakar, M.E. Isenkul, C.O. Sakar, A. Sertbas, F. Gurgen, S. Delil, H. Apaydin, O. Kursun, “Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings”, Journal of Biomedical and Health Informatics vol. 17, no. 4, pp. 828–834. 2013.
  • [13] B. Harel, M. Cannizzaro, P.J. Snyder, “Variability in fundamental frequency during speech in prodromal and incipient Parkinson’s disease: A longitudinal case study”, Brain Cognition vol. 56, no. 1, pp. 24-29, 2004.
  • [14] B. Shahbaba and R. Neal, Nonlinear models using Dirichlet process mixtures, Journal of Machine Learning Research vol. 10, pp. 1829-1850, 2009.
  • [15] P.F. Guo, P. Bhattacharya, N. Kharma, “Advances in detecting Parkinson’s disease”, Medical Biometrics vol. 6165, pp. 306-314, 2010.
  • [16] M.R. Daliri, “Chi-square distance kernel of the gaits for the diagnosis of Parkinson’s disease”, Biomedical Signal Processing and Control vol. 8, no. 1, pp. 66-70, 2013.
  • [17] D.C. Li, C.W. Liu, S.C. Hu, “A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets”, Artif Intell Med vol. 52, no. 1, pp. 45-52, 2011.
  • [18] C.O. Sakar and O. Kursun, “Telediagnosis of Parkinson’s disease using measurements of dysphonia”, Journal of Medical Systems vol. 34, no. 4, pp. 591-599, 2010.
  • [19] A. Ozcift and A. Gulten, “Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms”, Comput Meth Prog Bio vol. 104, no. 3, pp. 443-451, 2011.
  • [20] P. Luukka, “Feature selection using fuzzy entropy measures with similarity classifier”, Expert Syst Appl vol. 38, no. 4, pp. 4600-4607, 2011.
  • [21] G.S. Babu, S. Suresh, B.S. Mahanand, “A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson’s disease”, Expert Syst Appl vol. 41, no. 2, pp. 478-488, 2014.
  • [22] F.J. Martinez-Murcia, J.M. Gorriz, J. Ramirez, I.A. Illan, A. Ortiz and The Parkinson’s Progression Markers Initiative, “Automated Detection of Parkinsonism Using Significance Measures and Component Analysis in DatSCAN imaging”, Neurocomputing vol. 126, pp. 58-70, 2014.
  • [23] P. Ivens, A. Paulo, C. Donald, “The use of machine learning algorithms in recommender systems: A systematic review”, Expert Syst Appl, vol. 97, pp. 205-227, 2018.
  • [24] J. Cai, J. Luo, S. Wang, S. Yang, “Feature selection in machine learning: a new perspective. Neurocomputing”, vol. 300, pp. 70-79, 2018.
  • [25] L. Huan, H. Motoda, “Computational Methods of Feature Selection”, Chapman & Hall/Crc Data Mining and Knowledge Discovery Series, 2007.
  • [26] F. Mordelet, J. Horton, A.J. Hartemink, B.E. Engelhardt, R. Gordân, “Stability selection for regression-based models of transcription factor–DNA binding specificity”, Bioinformatics, 29:i117–i125, 2013.
  • [27] N. Meinshausen, P. Bühlmann, “Stability selection”, J. R. Statist Soc. B, vol. 72, no. 4, pp.417–473, 2010.
  • [28] Agresti A. An Introduction to Ccategorical Data Analysis. 2nd ed. New Jersey, USA: Wiley, 2007.
  • [29] Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. 2nd ed. Stanford, California: Springer, 2008.
  • [30] Kleinbaum DG, Klein M, Logistic Regression A Self-Learning Text, 3rd Edition, Springer 2010.
  • [31] L. Breiman, “Random forests”, Mach Learn, vol. 45, no 1, pp.5-32, 2011.
  • [32] J. Han, M. Kamber and J. Pei, Data Mining: Concepts and Techniques, 3rd ed., Waltham, MA, USA, 2012.
  • [33] S.A. Shaikh, “Measures derived from a 2x2 table for an accuracy of a diagnostic test”, J Biom Biostat vol. 2, no. 128, pp. 1-4, 2011.
  • [34] C.J. van Rijsbergen, Information retrieval. 2nd ed. London: Butterworths, 1979.
Birincil Dil en
Konular Mühendislik
Yayımlanma Tarihi Ocak 2020
Bölüm Makaleler
Yazarlar

Orcid: 0000-0002-2272-5243
Yazar: Kemal AKYOL (Sorumlu Yazar)
Kurum: KASTAMONU ÜNİVERSİTESİ
Ülke: Turkey


Orcid: 0000-0003-4719-8088
Yazar: Şafak BAYIR
Kurum: KARABÜK ÜNİVERSİTESİ
Ülke: Turkey


Orcid: 0000-0003-3577-2548
Yazar: Baha ŞEN
Kurum: YILDIRIM BEYAZIT ÜNİVERSİTESİ
Ülke: Turkey


Tarihler

Yayımlanma Tarihi : 28 Ocak 2020

Bibtex @araştırma makalesi { apjes541637, journal = {Akademik Platform Mühendislik ve Fen Bilimleri Dergisi}, issn = {}, eissn = {2147-4575}, address = {}, publisher = {Akademik Platform}, year = {2020}, volume = {8}, pages = {175 - 180}, doi = {10.21541/apjes.541637}, title = {Importance of Attribute Selection for Parkinson Disease}, key = {cite}, author = {AKYOL, Kemal and BAYIR, Şafak and ŞEN, Baha} }
APA AKYOL, K , BAYIR, Ş , ŞEN, B . (2020). Importance of Attribute Selection for Parkinson Disease. Akademik Platform Mühendislik ve Fen Bilimleri Dergisi , 8 (1) , 175-180 . DOI: 10.21541/apjes.541637
MLA AKYOL, K , BAYIR, Ş , ŞEN, B . "Importance of Attribute Selection for Parkinson Disease". Akademik Platform Mühendislik ve Fen Bilimleri Dergisi 8 (2020 ): 175-180 <https://dergipark.org.tr/tr/pub/apjes/issue/50706/541637>
Chicago AKYOL, K , BAYIR, Ş , ŞEN, B . "Importance of Attribute Selection for Parkinson Disease". Akademik Platform Mühendislik ve Fen Bilimleri Dergisi 8 (2020 ): 175-180
RIS TY - JOUR T1 - Importance of Attribute Selection for Parkinson Disease AU - Kemal AKYOL , Şafak BAYIR , Baha ŞEN Y1 - 2020 PY - 2020 N1 - doi: 10.21541/apjes.541637 DO - 10.21541/apjes.541637 T2 - Akademik Platform Mühendislik ve Fen Bilimleri Dergisi JF - Journal JO - JOR SP - 175 EP - 180 VL - 8 IS - 1 SN - -2147-4575 M3 - doi: 10.21541/apjes.541637 UR - https://doi.org/10.21541/apjes.541637 Y2 - 2019 ER -
EndNote %0 Akademik Platform Mühendislik ve Fen Bilimleri Dergisi Importance of Attribute Selection for Parkinson Disease %A Kemal AKYOL , Şafak BAYIR , Baha ŞEN %T Importance of Attribute Selection for Parkinson Disease %D 2020 %J Akademik Platform Mühendislik ve Fen Bilimleri Dergisi %P -2147-4575 %V 8 %N 1 %R doi: 10.21541/apjes.541637 %U 10.21541/apjes.541637
ISNAD AKYOL, Kemal , BAYIR, Şafak , ŞEN, Baha . "Importance of Attribute Selection for Parkinson Disease". Akademik Platform Mühendislik ve Fen Bilimleri Dergisi 8 / 1 (Ocak 2020): 175-180 . https://doi.org/10.21541/apjes.541637
AMA AKYOL K , BAYIR Ş , ŞEN B . Importance of Attribute Selection for Parkinson Disease. APJES. 2020; 8(1): 175-180.
Vancouver AKYOL K , BAYIR Ş , ŞEN B . Importance of Attribute Selection for Parkinson Disease. Akademik Platform Mühendislik ve Fen Bilimleri Dergisi. 2020; 8(1): 180-175.