Clinical informatics: Mining of pathological data by acoustic analysis

Zulfiqar Ali, Mansour Alsulaiman, Ghulam Muhammad, Ahmed Al-Nasheri, Awais Mahmood

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)


Data mining has a great potential in different areas of health informatics. Data mining in health industry can minimize the health cost as well as reduces the risk of life by informing a person at initial stage. An automatic classification system capable of mining pathological data may contribute in health informatics significantly. In this paper, an automatic system to differentiate between pathological and normal data is developed. The developed system mines the pathological data on the basis of an acoustic analysis. The purpose of the acoustic analysis is to estimate the auditory spectrum of a voice sample by using the principle of the human auditory system called as critical bandwidths. The estimated auditory spectrum simulates the behavior of a human ear and acts like an expert clinician who can identify a pathological voice by hearing. The pathological data used for this study is recorded from the people who are suffering from more than 100 different types of voice disorders. Voice of a disordered patient feels noisy, harsh, strain, breathy and unpleasant to ears. During the training phase of the proposed system, it takes labeled normal and pathological data to generate acoustic models by using the Gaussian mixture model. While in deployment phase, an unknown and unlabeled voice sample is given to the system to determine its type, i.e. normal or pathological. The best obtained accuracy of the system is 99.50%.

Original languageEnglish
Number of pages8
Publication statusPublished (in print/issue) - 13 Apr 2017
Event2017 International Conference on Informatics, Health and Technology, ICIHT 2017 - Riyadh, Saudi Arabia
Duration: 21 Feb 201723 Feb 2017


Conference2017 International Conference on Informatics, Health and Technology, ICIHT 2017
Country/TerritorySaudi Arabia


  • auditory spectrum
  • clinical data
  • data mining
  • Gaussian mixture model
  • Health informatics


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