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Ontology with SVM Based Diagnosis of Tuberculosis and Statistical Analysis

Murugavell Pandiyan


Osama El-Hassan


Amar Hassan Khamis


Pallikonda Rajasekaran

Murugavell Pandiyan 1

Osama El-Hassan 2
Amar Hassan Khamis 3
Pallikonda Rajasekaran 4

  1. Research Student, Kalasalingam University, Krishnan koil, India 1

  2. Head of eHealth Section, Dubai Health Authority, Dubai, UAE 2

  3. Professor, Mohammed Bin Rashid University of Medicine and Health Sciences 3

  4. Associate Professor, Kalasalingam University, Krishnan koil, India 4

Pages: 37-43

DOI: 10.18488/journal.9/2016.3.3/

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As per WHO report, Tuberculosis remains one of the world's deadliest communicable diseases. In 2013, an estimated 9.0 million developed TB and 1.5 million died from the disease, 360,000 of which whom were HIV positive. Tuberculosis is still a major problem in advanced countries due to specific socioeconomic factors. From a global perspective, many laboratories use the same methods today that were in use long time ago for the detection of tuberculosis, because most of innovative current technologies for the detection of tuberculosis incurs high cost and cannot be afforded for all the countries. The detection of tuberculosis remains a challenge from the point of diagnosis and confirmation and there is a growing need of accurate diagnosis process. In this research, an ontology based classification of tuberculosis laboratory tests, environmental factors and other vital signs are studied along with support vector machine for the diagnosis of the tuberculosis disease. Through this method, we are able to measure of the weightage of the disease, the future onset of the disease and produce, an alert. Ontology based classification is widely used for knowledge based information grouping and structuring while SVM is used for accurate and fast machine learning algorithm. By combining Ontology and the training data based on various characteristic of the tuberculosis are passed onto linear SVM. The results we are able to achieve with this method are helpful for diagnosis support and future onset.

Contribution/ Originality


Tuberculosis, Ontology, Machine learning, SVM, Decision support, Symptomatology.



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