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- Title
Applying Datamining Techniques to Predict Hearing Aid Type for Audiology Patients.
- Authors
ALJABERY, MAALIM A.; KURNAZ, SEFER
- Abstract
Our research is primarily based on dealing with different types of data using Data Mining (DM) techniques. In this research, we devoted ourselves to determining the type of Hearing Aid (HA) needed by patients with hearing impairment. HA type Diagnosis is a medical application that is a major challenge for researchers. Using DM techniques and Machine Learning (ML) has created a major challenge in the process of predicting the appropriate HA type for Audiology Patients (APs). Thus, this problem is primarily in the domain of classification problems. Our study makes a summary of some technical articles on determining the specific type of HA and introduces a study of using DM techniques to improve the accuracy predict for this purpose. Furthermore, our research includes the creation of a new Audiology Dataset based on the addition of some important fields on the old audiology database and analyses a new data of APs. These data have been obtained from the field work for nearly eight consecutive years, then extract a new classification based on this analysis. Relied on our search to reach the highest degree of accuracy in predicting the type of appropriate HA for APs who use it to enhance their hearing, we applied, compared, and analyzed the Neural Network (NN) and Support Vector Machine (SVM), applying Anaconda Navigator version 1.7.0, Orange Canvas version 3.13.0, and Spyder version 3.2.6 applications for Python coding.
- Publication
Journal of Information Science & Engineering, 2020, Vol 36, Issue 2, p205
- ISSN
1016-2364
- Publication type
Academic Journal
- DOI
10.6688/JISE.202003_36(2).0002