Abstract
This paper illustrates the utilise of various kind of machine learning approaches based on support vector machines for classifying Sickle Cell Disease data set. It has demonstrated that support vector machines generate an essential enhancement when applied for the pre-processing of clinical time-series data set. In this aspect, the objective of this study is to present discoveries for a number of classes of approaches for therapeutically associated problems in the purpose of acquiring high accuracy and performance. The primary case in this study includes classifying the dosage necessary for each patient individually. We applied a number of support vector machines to examine sickle cell data set based on the performance evaluation metrics. The result collected from a number of models have indicated that, support vector Classifier demonstrated inferior outcomes in comparison to Radial Basis Support Vector Classifier. For our Sickle cell data sets, it was found that the Parzen Kernel Support Vector Classifier produced the highest levels of performance and accuracy during training procedure accuracy 0.89733, AUC 0.94267. Where the testing set process, accuracy 0.81778, the area under the curve with 0.86556.
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References
Charache, S., et al.: Effect of hydroxyurea on the frequency of painful crises in sickle cell anemia. N. Engl. J. Med. 332(20), 1317–1322 (1995)
Khalaf, M., Hussain, A.J., Al-Jumeily, D., Fergus, P., Keenan, R., Radi, N.: A framework to support ubiquitous healthcare monitoring and diagnostic for sickle cell disease. In: Huang, D.-S., Jo, K.-H., Hussain, A. (eds.) ICIC 2015. LNCS, vol. 9226, pp. 665–675. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22186-1_66
Zaidan, A., et al.: A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution. Health Technol. 8(4), 223–238 (2018)
Adams, H.: Medical Informatics: Computer Applications in Health Care. JAMA 265(4), 522 (1991)
Khalaf, M., et al.: A data science methodology based on machine learning algorithms for flood severity prediction. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2018)
Taiana, M., Nascimento, J., Bernardino, A.: On the purity of training and testing data for learning: the case of pedestrian detection. Neurocomputing 150, 214–226 (2015)
Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: A survey of classification methods in data streams. In: Aggarwal, C.C. (ed.) data streams, vol. 31, pp. 39–59. Springer, Boston (2007). https://doi.org/10.1007/978-0-387-47534-9_3
Khalaf, M., et al.: A performance evaluation of systematic analysis for combining multi-class models for sickle cell disorder data sets. In: Huang, D.-S., Jo, K.-H., Figueroa-García, J.C. (eds.) ICIC 2017. LNCS, vol. 10362, pp. 115–121. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63312-1_10
Holder, L.B., Russell, I., Markov, Z., Pipe, A.G., Carse, B.: Current and future trends in feature selection and extraction for classification problems. Int. J. Pattern Recogn. Artif. Intell. 19(02), 133–142 (2005)
Khalaf, M., et al.: Recurrent neural network architectures for analysing biomedical data sets. In: 2017 10th International Conference on Developments in eSystems Engineering (DeSE), pp. 232–237. IEEE (2017)
Williams, N., Zander, S., Armitage, G.: A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. ACM SIGCOMM Comput. Commun. Rev. 36(5), 5–16 (2006)
Akay, M.F.: Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst. Appl. 36(2), 3240–3247 (2009)
Liu, Y., Yu, X., Huang, J.X., An, A.: Combining integrated sampling with SVM ensembles for learning from imbalanced datasets. Inf. Process. Manage. 47(4), 617–631 (2011)
Khalaf, M., et al.: training neural networks as experimental models: classifying biomedical datasets for sickle cell disease. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2016. LNCS, vol. 9771, pp. 784–795. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42291-6_78
Subashini, T., Ramalingam, V., Palanivel, S.: Breast mass classification based on cytological patterns using RBFNN and SVM. Expert Syst. Appl. 36(3), 5284–5290 (2009)
Gil, D., Manuel, D.J.: Diagnosing Parkinson by using artificial neural networks and support vector machines. Global J. Comput. Sci. Technol. 9(4), 63–71 (2009)
Dalvi, P.T., Vernekar, N.: Anemia detection using ensemble learning techniques and statistical models. In IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 1747–1751. IEEE (2016)
Tang, J., Zhang, X.: Prediction of smoothed monthly mean sunspot number based on chaos theory. Acta Phys. Sin. 61, 169601 (2012)
Seliya, N., Khoshgoftaar, T.M., Van Hulse, J.: Aggregating performance metrics for classifier evaluation. In: IEEE International Conference on Information Reuse & Integration, IRI 2009,, pp. 35–40. IEEE (2009)
Khalaf, M., et al.: Machine learning approaches to the application of disease modifying therapy for sickle cell using classification models. Neurocomputing 228, 154–164 (2017)
Wei, Z.-S., Han, K., Yang, J.-Y., Shen, H.-B., Yu, D.-J.: Protein–protein interaction sites prediction by ensembling SVM and sample-weighted random forests. Neurocomputing 193, 201–212 (2016)
Sánchez A, V.D.: Advanced support vector machines and kernel methods. Neurocomputing 55(1–2), 5–20 (2003)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Adankon, M.M., Cheriet, M.: Model selection for the LS-SVM. Application to handwriting recognition. Pattern Recogn. 42(12), 3264–3270 (2009)
Gunn, S.R.: Support vector machines for classification and regression. ISIS technical report, vol. 14, no. 1, pp. 5–16 (1998)
Khalaf, M., et al.: The utilisation of composite machine learning models for the classification of medical datasets for sickle cell disease. In: 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC), pp. 37–41. IEEE (2016)
Hric, M., Chmulík, M., Jarina, R.: Model parameters selection for SVM classification using Particle Swarm Optimization. In: Radioelektronika (RADIOELEKTRONIKA), 2011 21st International Conference, 2011, pp. 1–4. IEEE (2011)
Baker, T., Rana, O.F., Calinescu, R., Tolosana-Calasanz, R., Bañares, J.Á.: Towards autonomic cloud services engineering via intention workflow model. In: Altmann, J., Vanmechelen, K., Rana, Omer F. (eds.) GECON 2013. LNCS, vol. 8193, pp. 212–227. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02414-1_16
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The authors would like to thank Al-Maarif University College for supporting this research.
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Khalaf, M. et al. (2019). An Application of Using Support Vector Machine Based on Classification Technique for Predicting Medical Data Sets. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_55
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