Quick Menu

Upcoming Journals

Journal of Scientific Research and Reports

Journal of Scientific Research and Reports, ISSN: 2320-0227,Vol.: 3, Issue.: 23 (01-15 December)


Application of Data Mining Techniques to Audiometric Data among Professionals in India


J. Majumder1 and L. K. Sharma1*

1National Institute of Occupational Health (ICMR), Ahmedabad-380016, India.


Article Information


(1) Prof. William Ebomoyi, Department of Health Studies, College of Health Sciences, Chicago State University, USA.


(1) Anonymous

(2) Anonymous

(3) Anonymous

Complete Peer review History: http://www.sciencedomain.org/review-history/6134




Aims: Noise induced hearing loss (NIHL) is among the principal occupational health hazard. To illustrate that, in order to enrich the database on audiometric status and fast dissemination of knowledgebase, data mining techniques are imperative tools.
Study Design: A cross sectional study design was used.
Place and Duration of Study: Pure tone audiometric data of both ears of drivers that have 10 years working experience and office workers from Kolkata City, India were recorded.
Methodology: The data were subjected to both unsupervised and supervised learning techniques, in turn, in order to train the classifier that determines the clusters for newly generated cases. Expectation Maximization (EM), k-means, Linear Vector Quantization (LVQ), and Self Organization Map (SOM) unsupervised learning techniques were utilized.
Results: Silhouette Plot (SP) validation showed that 93.3% of the considered cases for the left ear and 85.8% for the right ear were correctly classified. These metadata were further subjected to supervised learning algorithm to achieve a high level correctly classified result, in which, each cluster bears its class label. Naïve Bays Classifier (NBC) recorded, as accurate (98.8%) for both left and right ears. The high accuracy of supervised learning algorithms, cross validated with 10-fold cross validation tends to predict the class of audiometric data whenever a newly generated data are introduced.
Conclusion: This feasibility of using machine learning and data classification models on the audiometric data would be an effective tool in the hearing conservation program for individuals exposed to noisy environments in their respective workplaces.


Keywords :

Hearing threshold; cluster analysis; unsupervised learning; supervised learning; cross validation.


Full Article - PDF    Page 2960-2971    Article Metrics


DOI : 10.9734/JSRR/2014/12700

Review History    Comments

Search this site

Advanced Search

Announcement & News

Nature (Impact Factor: 41.6) confirmed high standard of SDI journal and its editors

We are happy to inform that Nature (Impact Factor: 41.6) confirmed high standard of SDI journal and ...

ISI Thomson Reuters selected British Journal of Pharmaceutical Research for Emerging Sources Citation Index

We are delighted to inform that ISI Thomson Reuters selected British Journal of Pharmaceutical Resea...

SCOPUS selected Annual Research & Review in Biology (ARRB)

We are delighted to inform that famous indexing organization SCOPUS (from Elsevier) selected  A...

Index Copernicus Evaluation Result Released

We are delighted to inform that Index Copernicus (a leading indexing organization from Pol...

Journal Repository (JR): Permanent Digital Archiving of SDI journals

SDI is happy to announce that all our journals are now permanently archived in Journal Repository (J...

SDI journal got 35th ranking in Publons

We are delighted to announce (as of 04/01/2016) that British Journal of Medicine and Medical Re...

Growth of SDI and world publication market

As of 2014, total 25,064 journals are competing in World market of journal publication. In 2011, tot...

Science (IF: 31) report confirmed the high standard of SDI journal

As per a recent report (Link) of Science journal (present Impact factor 31), one of our journal (Bri...

SDI introduced Post-publication peer review by its comment section

SDI journals encourage Post-publication peer review by its comment section   Policy details a...

SDI promotes transparent Advanced OPEN peer review

We have migrated to transparent and toughest ‘Advanced OPEN peer review’ system (...


  • No Awards listed.

Browser Compatibility : Mozila firefox, Google Crome and IE 7 & above. Creative Commons License Terms & Condition   |   Privacy Policy   |   Join Us   |   Help   |   Contact Us
© Copyright 2010-2018, SCIENCEDOMAIN international. All rights reserved.