Comparison of Support Vector Machine Models in the Classification of Susceptibility to Schistosomiasis

Öz

Schistosomiasis has become endemic sending millions of people into untimely graves. A lot of contributing efforts in term of research has been made to eradicate or reduce the rate of this dangerous infection. In this research work the concept of Machine Learning as one of the sub-division of Artificial Intelligence, is being used to determine the level of susceptibility of Schistosomiasis. The research made a comparison of the various support vector machine models as useful tools in the Machine Learning to determine the level of susceptibility of Schistosomiasis. The results obtained which include Confusion Matrix (CM), Receiver Operating Character (ROC), and Parallel Coordinate Plot were interpreted in form of accuracy, processing speed and execution time. It was finally concluded that Medium Gaussian is the best of all the six models considered.

Anahtar Kelimeler

• Artificial Intelligence
• SVM
• Classification
• Schistosomiasis

Kaynakça

1. [1] U. A. Makolo and M. O. Akinyemi, “Predicting the risk of infection with schistosoma heamatobium using ML,” International Journal of Computer Application (0975 – 8887) Vol. 136, no 8, 2016F. Kentli, M. Yilmaz. "Mathematical modelling of two-axis photovoltaic system with improved efficiency." Elektronika Ir Elektrotechnika, vol. 21. 4, 2015, pp 40-43. [2] H. Reynold and L. W. Arve. “Station of schistomiasis elimination in the carribbian region”. Tropical Medical and Infection Disease, 2019 [3] K. Amit. “Artificial Intellenge and soft computing behavioural and cognative modelling of the human brain”. CRIC Press, Boca ration London, New York, Washington, D. C, 1999 [4] P. M. Kelvin. “Machine Learning a propabilistic perspective”. The MIT Press Cambridge, Mossachusetts, London, England, 2008. [5] J. Vikramadiya. “Support Vector Machine: A review”. School of FFCS Washington State University, Pulman, pp 99164, 2006. [6] S. N. William. “What is a support vector machine”. PRIMEK: Computation Biology/Mature Biotechnology, Vol. 24, no 12, 2006 [7] K. Stefanie, L. B. Soren, J. I. Katrin, K. Jennifer and U. Jurg. “Diagnosis and treatment of Schistomiasis in children in the era of intensified central”. Expert reviews, 2013 [8] L. Guo, Z. Xiaorong, L. Jianbiy, Z. Hengtao, C. Yanyan, L. Jianhua, J. Hengbo, Y. Junsing, and N. Shaofa. PLOS Neglected Tropical Diseases, 2018 [9] A. Noura, Heart Diseases Diagnoses using Artificial Neural Network, Network and Complex System, ISSN 2224-610X (Paper) ISSN 2225-0603 (Online), Vol.5, No.4, 2015 [10] Bakpo, F. S.and Kabari, L. G, Diagnosing Skin Diseases Using an Artificial Neural Network. Available at: http://cdn.intechopen.com/pdfs-wm/14893.pdf [11] B. M. G. Awosa, O. K. Olalere, K. A. Kawonise, A. O. Fabiyi and A. A. Fabiyi. “Expert system for diagnosis and management of kidney diseases”. International Journal of Computer Trends and technology, Vol. 20 no. 3, 2015. [12] S. Ramya and N. Radha. “Diagnosis of chronic kidney disease using ML Algorithm”. International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issues 1, 2016 [13] V. Deepti and S. Sheetal. “Classification of heart diseases using sum and ANN”. International Journal of Research in IJRCCT Computer & Communication Technology, Vol.2, Issue 9, 2013. [14] K. Prasnasti and R. S. Disha. “Prediction of carchovascular diseased using support vector machine and Bayesian classification”. International Journal of Computer Application, Vol 156, no 2, 2016. [15] A. Sheikh, V. D., Bhagile, R. R. Manza, R. J. Ramtele. “Diagnosis and medical prescription of Heart Disease using support vector machine and feed forward back propagation technique”. International Journal of Computer Science and Engineering Vol. 2, No. 6, 2010. [16] G. Shashikant, P. Chetan, and G. Ashok. “Heart disease diagnosis using support vector machine”. International Conference on Computer Science and Information Technology, Pattaya, 2011., Geneva, Switzerland, 1993.