Ontology with SVM Based Diagnosis of Tuberculosis and Statistical Analysis
0 likes28 views
The document discusses a study presented at the 5th International Scientific Conference on Applied Sciences and Engineering in 2015, focusing on the diagnosis of tuberculosis using ontology-based classification and support vector machines (SVM). It highlights the ongoing challenge of tuberculosis detection despite its high fatality rates, and the limitations of current diagnostic technologies. The research aims to enhance diagnosis accuracy and predict disease onset by analyzing laboratory tests and environmental factors.
1 of 1
Download to read offline
More Related Content
What's hot (20)
Ad
Similar to Ontology with SVM Based Diagnosis of Tuberculosis and Statistical Analysis (20)
Ad
More from Conferenceproceedings (20)
Recently uploaded (20)
Ontology with SVM Based Diagnosis of Tuberculosis and Statistical Analysis
- 1. Abstract of Applied Sciences and Engineering, 2015, Vol.5 DOI: 10.18488/journal.1001/2015.5/1001.5 5th International Scientific Conference on Applied Sciences and Engineering 29-30 December, 2015 Flora Grand Hotel, Dubai, United Arab Emirates Conference Website: www.scihost.org 12 Paper ID: 81/15/ 5 th ISCASE Ontology with SVM Based Diagnosis of Tuberculosis and Statistical Analysis P Murugavell1 --- Osama El Hassan2 --- Pallikonda Rajasekaran3 Abstract 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 afford 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 on 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, future onset of the disease and produces 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 on this method is helpful for diagnosis support and future onset. Keywords: Statistics, Ontology, Machine learning, Infectious disease, SVM, Decision support