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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’...

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主要な著者: G. Noma, Nasir, Mohd Khanapi, Abd Ghani, Mohamad Khir , Abdullah, Noorizan , Yahya
フォーマット: 論文
言語:English
出版事項: 2013
主題:
QA76 Computer software
オンライン･アクセス:http://eprints.utem.edu.my/id/eprint/9888/1/NasirNoma-MKAG-AJBAS.pdf
http://eprints.utem.edu.my/id/eprint/9888/
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spelling my.utem.eprints.98882015-05-28T04:07:25Z http://eprints.utem.edu.my/id/eprint/9888/ Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier G. Noma, Nasir Mohd Khanapi, Abd Ghani Mohamad Khir , Abdullah Noorizan , Yahya QA76 Computer software 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’s vocabulary from patterns generated by FP-Growth algorithm was explored. Both multivariate Bernoulli and multinomial naïve Bayes models were used with and without the feature extraction. The results were validated with repeated random sub-sampling validation performed using 5 partitions with 10, 20, 30, 40 and 50 training examples respectively averaged over 10 iterations. The multivariate Bernoulli model with feature extraction is found to be more accurate in predicting hearing loss symptoms with average error rate of only 0, 0.5, 1, 1.75 and 5.4% for the partitions with 10, 20, 30, 40 and 50 training examples respectively compared to multinomial model with feature extraction. However, the two models with feature extraction produce better results than same models without feature extraction. 2013-07 Article PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/9888/1/NasirNoma-MKAG-AJBAS.pdf G. Noma, Nasir and Mohd Khanapi, Abd Ghani and Mohamad Khir , Abdullah and Noorizan , Yahya (2013) Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier. Australian Journal of Basic and Applied Sciences (AJBAS). pp. 35-43. ISSN 1991-8178
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
G. Noma, Nasir
Mohd Khanapi, Abd Ghani
Mohamad Khir , Abdullah
Noorizan , Yahya
Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier
description 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’s vocabulary from patterns generated by FP-Growth algorithm was explored. Both multivariate Bernoulli and multinomial naïve Bayes models were used with and without the feature extraction. The results were validated with repeated random sub-sampling validation performed using 5 partitions with 10, 20, 30, 40 and 50 training examples respectively averaged over 10 iterations. The multivariate Bernoulli model with feature extraction is found to be more accurate in predicting hearing loss symptoms with average error rate of only 0, 0.5, 1, 1.75 and 5.4% for the partitions with 10, 20, 30, 40 and 50 training examples respectively compared to multinomial model with feature extraction. However, the two models with feature extraction produce better results than same models without feature extraction.
format Article
author G. Noma, Nasir
Mohd Khanapi, Abd Ghani
Mohamad Khir , Abdullah
Noorizan , Yahya
author_facet G. Noma, Nasir
Mohd Khanapi, Abd Ghani
Mohamad Khir , Abdullah
Noorizan , Yahya
author_sort G. Noma, Nasir
title Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier
title_short Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier
title_full Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier
title_fullStr Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier
title_full_unstemmed Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier
title_sort predicting hearing loss symptoms from audiometry data using fp-growth algorithm and bayesian classifier
publishDate 2013
url http://eprints.utem.edu.my/id/eprint/9888/1/NasirNoma-MKAG-AJBAS.pdf
http://eprints.utem.edu.my/id/eprint/9888/
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score 13.251813

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