A study on diagnosis of Parkinson’s disease from voice dysphonias
Автор: Kemal Akyol
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 6 Vol. 10, 2018 года.
Бесплатный доступ
Parkinson disease that occurs at older ages is a neurological disorder that is one of the most painful, dangerous and non-curable diseases. One symptom that a person may have Parkinson’s disease is trouble that can occur in the voice of a person which is so-called dysphonia. In this study, an application based on assessing the importance of features was carried out by using multiple types of sound recordings dataset for diagnosis of Parkinson disease from voice disorders. The sub-datasets, which were obtained from these records and were divided into 70-30% training and testing data respectively, include the important features. According to the experimental results, the Random Forest and Logistic Regression algorithms were found successful in general. Besides, one or two of these algorithms were found to be more successful for each sound. For example, the Logistic Regression algorithm is more successful for the ‘a’ voice. The Artificial Neural Networks algorithm is more successful for the ‘o’ voice.
Importance of feature, parkinson disease, recursive feature elimination, voice dysphonias
Короткий адрес: https://sciup.org/15016270
IDR: 15016270 | DOI: 10.5815/ijitcs.2018.06.04
Список литературы A study on diagnosis of Parkinson’s disease from voice dysphonias
- C.O. Sakar, O. Kursun, “Telediagnosis of Parkinson's disease using measurements of dysphonia,” J Med Syst, Vol. 34, pp. 591-599, 2010.
- M.E. Isenkul, B. Erdoğdu, C.O. Şakar, E. Gümüş, M.Ş. Delil, F. Gürgen, A. Sertbaş, O. Kursun, “Building a speech database and using speech patterns for diagnosis of Parkinson’s disease from voice dysphonias,” In: Tıptekno’11 Tıp Teknolojileri Ulusal Kongresi, Antalya, Turkey.
- M.A. Little, P.E. McSharry, E.J. Hunter, J. Spielman, L.O. Ramig, “Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease,” In: IEEE T Bio-Med Eng, Vol. 56, pp. 1015-1022, 2009.
- 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,” In: IEEE Journal of Biomedical and Health Informatics, Vol. 17, pp. 828-834, 2013.
- B. Mahnaz, S. Ashkan, “A multiple-classifier framework for Parkinson's disease detection based on various vocal tests,” International Journal of Telemedicine and Applications, Vol. 6837498, pp.1-9, 2016.
- A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig, “Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests,” In: IEEE T Bio-Med Eng, Vol. 57, pp. 884-893, 2010.
- S. Lahmiri, “Parkinson’s disease detection based on dysphonia measurements,” Physica A, Vol. 471, pp. 98-105, 2017.
- G.K. Sewall, J. Jiang, C.N. Ford, “Clinical evaluation of Parkinson’s-related dysphonia,” Laryngoscope, Vol. 116, pp. 1740-1744, 2006.
- Ahmed F. Alia, Adel Taweel,"Feature Selection based on Hybrid Binary Cuckoo Search and Rough Set Theory in Classification for Nominal Datasets", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.4, pp.63-72, 2017. DOI: 10.5815/ijitcs.2017.04.08
- Yihui Liu, Uwe Aickelin,"Feature Selection in Detection of Adverse Drug Reactions from the Health Improvement Network (THIN) Database", IJITCS, vol.7, no.3, pp.68-85, 2015. DOI: 10.5815/ijitcs.2015.03.10
- A. Enshaei and J. Faith, “Feature Selection with Targeted Projection Pursuit,” I.J. Information Technology and Computer Science, vol. 7, no. 5, pp. 34-39, 2015. DOI: 10.5815/ijitcs.2015.05.05
- I. Guyon, J. Weston, S. Barnhill, V. Vapnik, “Gene selection for cancer classification using support vector machines,” Mach Learn, Vol. 46, pp. 389-422, 2002.
- I. Portugal, P. Alencar, D. Cowan, “The use of machine learning algorithms in recommender systems: A systematic review,” Expert Systems with Applications, 2018, Vol. 97, pp. 205-227.
- S. R. Priyanka Shetty, Sujata Joshi,"A Tool for Diabetes Prediction and Monitoring Using Data Mining Technique", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.11, pp.26-32, 2016. DOI: 10.5815/ijitcs.2016.11.04
- Harjot Kaur, Mandeep Kaur,"A Hybrid Approach for Blur Detection Using Naïve Bayes Nearest Neighbor Classifier", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.12, pp.75-82, 2016. DOI: 10.5815/ijitcs.2016.12.09
- L. Breiman, “Random forests,” Mach Learn, Vol. 45, pp. 5-32, 2001.
- O. Akar, O. Gungor, “Classification of multispectral images using Random Forest algorithm,” Journal of Geodesy and Geoinformation, Vol. 1, pp. 139-146, 2012.
- S. Lemeshow, D. Hosmer, Applied Logistic Regression, 2nd ed. New York, USA: Wiley, 2000.
- A. Agresti, An Introduction to Categorical Data Analysis, 2nd ed. New Jersey, USA: Wiley, 2007.
- K. Gurney, An Introduction to Neural Networks. London and New York, USA: UCL Press, 1997.
- S. Haykin, Neural Networks and Learning Machines. 3rd ed., New York, USA: Prentice Hall, 2009.
- J. Han, M. Kamber, J. Pei, Data Mining Concepts and Techniques, 3rd ed, Waltham, USA: Elsevier, 2012.
- M. Bramer, Principles of Data Mining, Undergraduate Topics in Computer Science. 2nd ed. London: Springer, 2013.
- 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.
