A comparative study of support vector machine and logistic regression for the diagnosis of thyroid dysfunction

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Thyroid is one of the vital diseases that influence individuals of any age group now a day. Infections of the thyroid, incorporate conditions related with extreme release of thyroid hormones (Hyper thyroidism) which is likewise called thyrotoxicosis and those related with thyroid hormone insufficiency (Hypothyroidism). Expectation of these two sorts of thyroid disease is critical for thyroid analysis. In this paper, support vector machines and logistic regression are proposed for predicting patients with thyrotoxicosis and without thyrotoxicosis. The outcomes demonstrate that, logistic regression perform well over support vector machine with 98.92% exactness.


  • Keywords


    Logistic Regression; Precision; Recall; Support Vector Machine; Thyrotoxicosis.

  • References


      [1] FarhadSoleimanianGharehchopogh, et al. “Using Artificial Neural Network in Diagnosis of Thyroid Disease: A Case Study” .International Journal on Computational Sciences & Applications 2013; 3(4).

      [2] K.Saravana Kumar, Dr. R. ManickaChezian, “Support Vector Machine and K- Nearest Neighbor Based Analysis for the Prediction of Hypothyroid”, International Journal of Pharma and Bio Sciences 2014; 5(4), pp 447 – 453.

      [3] M. R. NazariKousarrizi, F.Seiti, and M.Teshnehlab, “An Experimental Comparative Study on Thyroid Disease Diagnosis Based on Feature Subset Selection and classification”, International Journal of Electrical & Computer Sciences IJECS, Vol: 12 No. 01, February (2012), pp 13-20.

      [4] Li-Na Li, et al. “A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine”, Journal of Medical Systems 2012; 36(5), pp 3327-3337.https://doi.org/10.1007/s10916-012-9825-3.

      [5] Baydaa S. B. Alyas, “Design an Intelligent System for Thyroid Diseases Diagnosis”, International Journal of Enhanced Research in Science Technology & Engineering 2014; 3 (4),pp 217-229.

      [6] Ahmad Taher Azar, et al. “Fuzzy and hard clustering analysis for thyroid disease”, Computer Methods and Programs in Biomedicine 2013; 111(1),pp 1-16.https://doi.org/10.1016/j.cmpb.2013.01.002.

      [7] Ms.WrushaliMendre, Dr.RanjanaD.Raut, “Neural Network based Decision Support System for the Diagnosis of Thyroid Diseases”, International Journal of Computer Science and Applications 2013; 6(2), pp 102-106.

      [8] Hui-Ling Chen, et al. “A Three-Stage Expert System Based on Support Vector Machines for Thyroid Disease Diagnosis” Journal of Medical Systems 2012; 36, pp 1953-1963.https://doi.org/10.1007/s10916-011-9655-8.

      [9] Kenji Hoshi, et al. “An Analysis of Thyroid Function Diagnosis Using Bayesian-Type and SOM-Type Neural Networks”,Chemical & Pharmaceutical Bulletin; 200653(12), pp 1570-1574.https://doi.org/10.1248/cpb.53.1570.

      [10] L. Ozyılmaz, T. Yıldırım, “Diagnosis of thyroid disease using artificial neural network methods”, Proceedings of ICONIP’02 9th International Conference on Neural Information Processing. Orchid Country Club. Singapore 2002, pp 2033– 2036.https://doi.org/10.1109/ICONIP.2002.1199031.

      [11] www.ics.uci.edu/pub/ml-repos/machine-learningdatabases/, 2001.

      [12] H. Yusuff, et al. “Breast Cancer Analysis Using Logistic Regression”, IJRRAS 10 (1) January 2012,pp 14-22.

      [13] CHAO-YING JOANNE PENG,et al. “An Introduction to Logistic Regression Analysis and Reporting”, The Journal of Educational Research, September/October 2002 [Vol. 96(No. 1)], pp 3-14.

      [14] Terrence S. Furey, et al. “Support vector machine classification and validation of cancer tissue samples using microarray expression data”, Bioinformatics, 2000, Vol.16, no.10, pp 906-914.https://doi.org/10.1093/bioinformatics/16.10.906.


 

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Article ID: 9714
 
DOI: 10.14419/ijet.v7i1.1.9714




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