A Predictive Symptoms-based System using Support Vector Machines to enhanced Classification Accuracy of Malaria and Typhoid Coinfection

Автор: Enesi Femi Aminu, Emmanuel Onyebuchi Ogbonnia, Ibrahim Shehi Shehu

Журнал: International Journal of Mathematical Sciences and Computing(IJMSC) @ijmsc

Статья в выпуске: 4 vol.2, 2016 года.

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High costs of medical equipment and insufficient number of medical specialists have immensely contributed to the increment of death rate especially in rural areas of most developing countries. According to Roll Back Malaria there are 300 million acute cases of malaria per year worldwide, causing more than one million deaths. About 90% of these deaths happen in Africa, majorly in young children. Besides malaria when tested; a large number is coinfected with typhoid. Most often, symptoms of malaria and typhoid fevers do have common characteristics and clinicians do have difficulties in distinguishing them. For instance in Nigeria the existing diagnostic systems for malaria and typhoid in rural settlements are inefficient thereby making the result to be inaccurate and resulting to treatment of wrong ailments. Therefore in this paper, a predictive symptoms-based system for malaria and typhoid coinfection using Support Vector Machines (SVMs) is proposed for an improved classification results and the system is implemented using Microsoft Visual Basic 2013. Relatively high performance accuracy was achieved when tested on a reserved data set collected from a hospital. Hence the system will be of a great significant use in terms of affordable and quality health care services especially in rural settlement as an alternative and a reliable diagnostic system for the ailments.

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Malaria, Typhoid, Support Vector Machines, Coinfection, Microsoft Visual Basic

Короткий адрес: https://sciup.org/15010295

IDR: 15010295

Список литературы A Predictive Symptoms-based System using Support Vector Machines to enhanced Classification Accuracy of Malaria and Typhoid Coinfection

  • Edicha JA, Hassan SM and Ocholi J. "An Analysis of Long Lasting insecticidal Treated Nets (LLIN) in Chanchaga LGA, Niger State, Nigeria". Confluence Journal of Environmental Studies, 9, 8-23. 2014.
  • Panchbhai VV, Damahe LB, Nagpure AV, and Chopkar PN. "RBCs and Parasites Segmentation from Thin Smear Blood Cell Images" I.J. Image, Graphics and Signal Processing, 2012, 10, 54-60.
  • Oguntimilehin AO, Adetunmbi, and Olatunji KA. "A Machine Learning Based Clinical Decision Support System for Diagnosis and Treatment of Typhoid Fever" International Journal of Advanced Research in Computer Science and Software Engineering Volume 4, Issue 6, June 2014.
  • Fatumo SA, Adetiba E and Onaolapo JO. "Implementation of xpertmaltyph: an expert system for medical diagnosis of the complications of malaria and typhoid". IOSR Journal of Computer Engineering (IOSR-JCE), ISSN: 2278-8727Volume 8, Issue 5.2013.
  • Scribd "Typhoid Fever Paper". From http://www.scribd.com/doc/267868534/Typhoid-Fever-Paper#scribd Retrieved on 16 October 2015.
  • Onyido AE, Ifeadi CP, Umeanato PU, Aribodor DN, Ezeanya LC, and Ugha CN. "Co-infection of malaria and typhoid fever in Ekwuhumili community Anambra State, Southeastern Nigeria." New York Sci J. 2014; 7(7):18-27.2012.
  • Pradhan P. "Coinfection of Typhoid and Malaria" Journal of Medical Laboratory and Diagnosis Vol. 2(3) pp. 22-26, July 2011.
  • Ali W, Shamsuddin SM and Ismail AS. "Web Proxy Cache Content Classification based on Support Vector Machine" Journal of Artificial Intelligence 4 (1): 100 – 109, 2011.
  • Wang L. "Support Vector Machines: theory and applications". Springer Science &Business Media (Vol. 177) 2005.
  • Dash S, Patra B, and Tripathy BK. "A Hybrid Data Mining Technique for Improving the Classification Accuracy of Microarray Data Set." I.J. Information Engineering and Electronic Business, 2012, 2, 43-50
  • Jiang H, Wai-Ki C, and Zheng Z. "Kernel Techniques in Support Vector Machines for Classification of Biological Data" I.J. Information Technology and Computer Science, 2011, 2, 1-8.
  • Oguntimilehin A, Adetunmbi AO, and Abiola OB. "A Review of Predictive Models on Diagnosis and Treatment of Malaria Fever" IJCSMC, Vol. 4, Issue. 5, May 2015, pg.1087 – 1093.
  • Yang ZR. "Biological Applications of Support Vector Machines" Briefings in Bioinformatics. Vol 5. no 4. 328–338. December 2004.
  • Chayadevi ML and Raju GT. "Usage of art for automatic malaria parasite identification based on fractal features". International Journal of Video&Image Processing and Network Security IJVIPNS-IJENS Vol: 14 No:0. 2014.
  • Widodo S and Wijiyanto. "Texture Analysis to Detect Malaria Tropica in Blood Smears Image using Support Vector Machine". International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163 Volume 1 Issue 8. 2014.
  • Suryawanshi S and Dixit VV. "Comparative Study of Malaria Parasite Detection Using Euclidean Distance Classifier & SVM". International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2 Issue. 2013.
  • Yaser SA, Malik M and Lin H. Learning from data. New York: AMLBooks. 2012.
  • Yaser A-M. Learning from data: Introductory Machine Learning Course. Caltech. 2012.
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