Applying datamining techniques to predict hearing aid type for audiology patients
| dc.contributor.author | Aljabery, Maalim A. | |
| dc.contributor.author | Kurnaz, Sefer | |
| dc.date.accessioned | 2021-05-15T11:33:47Z | |
| dc.date.available | 2021-05-15T11:33:47Z | |
| dc.date.issued | 2020 | |
| dc.department | Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.description.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. | en_US |
| dc.identifier.doi | 10.6688/JISE.202003_36(2).0002 | |
| dc.identifier.endpage | 215 | en_US |
| dc.identifier.issn | 1016-2364 | |
| dc.identifier.issue | 2 | en_US |
| dc.identifier.scopus | 2-s2.0-85082813914 | |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.startpage | 205 | en_US |
| dc.identifier.uri | https://doi.org/10.6688/JISE.202003_36(2).0002 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/229 | |
| dc.identifier.volume | 36 | en_US |
| dc.identifier.wos | WOS:000523607200002 | |
| dc.identifier.wosquality | Q4 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Kurnaz, Sefer | |
| dc.language.iso | en | |
| dc.publisher | Inst Information Science | en_US |
| dc.relation.ispartof | Journal of Information Science and Engineering | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Data Mining | en_US |
| dc.subject | Hearing Aid | en_US |
| dc.subject | Audiology Patient | en_US |
| dc.subject | Neural Network | en_US |
| dc.subject | Support Vector Machines | en_US |
| dc.title | Applying datamining techniques to predict hearing aid type for audiology patients | |
| dc.type | Article |
