Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier
This paper presents the results of applying machine learning algorithms to predict hearing loss symptoms given air and bone conduction audiometry thresholds. FP-Growth (frequent pattern growth) algorithm was employed as a feature extraction technique. The effect of extracting naïve Bayes classifier’...
محفوظ في:
المؤلفون الرئيسيون: | G. Noma, Nasir, Mohd Khanapi, Abd Ghani, Mohamad Khir , Abdullah, Noorizan , Yahya |
---|---|
التنسيق: | مقال |
اللغة: | English |
منشور في: |
2013
|
الموضوعات: | |
الوصول للمادة أونلاين: | http://eprints.utem.edu.my/id/eprint/9888/1/NasirNoma-MKAG-AJBAS.pdf http://eprints.utem.edu.my/id/eprint/9888/ |
الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
مواد مشابهة
-
Identifying Relationship between Hearing loss Symptoms and Pure-tone Audiometry Thresholds with FP-Growth Algorithm
بواسطة: G. Noma, Nasir, وآخرون
منشور في: (2013) -
Discovering Pattern in Medical Audiology
Data with FP-Growth Algorithm
بواسطة: G. Noma, Nasir, وآخرون
منشور في: (2012) -
Identification model for hearing loss symptoms using machine learning techniques
بواسطة: Nasiru Garba Noma
منشور في: (2014) -
Hearing Threshold in Audiometry Testing: Pure Tone Versus Warble Tone.
بواسطة: Md Zakaria, Nur Annisa Rohaya, وآخرون
منشور في: (2021) -
Detection of high-frequency hearing loss among hospital staffs exposed to occupational noise using extended pure tone audiometry
بواسطة: Umbaik, Norsyamira Aida Mohamad
منشور في: (2019)