Abstract
The paper proposes a method for diagnosing Parkinson’s disease using a reduced set of patients’ voice features samples. The Sequential Forward Selection and Sequential Backward Selection methods were used to reduce features. The data reduced were classified separately with the use of over a dozen popular classifiers. The effectiveness of diagnosing Parkinson’s disease on a reduced set of data was determined for each classifier. Then it was compared with the results obtained for the data without any reduction of features.
The experiments carried out showed that, depending on the classifier used, the reduction of the set even to few features allowed increasing the effectiveness of classification. The research also allowed indicating the classifiers and features, with the use of which the best results of classification were obtained. The experiments were carried out on a publicly available database containing voice samples of patients with Parkinson’s disease and of healthy patients.
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Wrobel, K. (2019). Diagnosing Parkinson’s Disease with the Use of a Reduced Set of Patients’ Voice Features Samples. In: Saeed, K., Chaki, R., Janev, V. (eds) Computer Information Systems and Industrial Management. CISIM 2019. Lecture Notes in Computer Science(), vol 11703. Springer, Cham. https://doi.org/10.1007/978-3-030-28957-7_8
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