Abstract
Machine learning approach is considered as a field of science aiming specifically to extract knowledge from the data sets. The main aim of this study is to provide a sophisticate model to difference applications of machine learning models for medically related problems. We attempt for classifying the amount of medications for each patient with Sickle Cell disorder. We present a new technique to combine two classifiers between the Levenberg-Marquartdt training algorithm and the k-nearest neighbours algorithm. In this paper, we introduce multi-class label classification problem in order to obtain training and testing methods for each models along with other performance evaluations. In machine learning, the models utilise a training sets in association with building a classifier that provide a reliable classification. This research discusses different aspects of machine learning approaches for the classification of biomedical data. We are mainly focus on the multi-class label classification problem where many number of classes are available in the data sets. Results have indicated that for the machine learning models tested, the combination classifiers were found to yield considerably better results over the range of performance measures that been selected for this research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Weatherall, D.J.: The importance of micromapping the gene frequencies for the common inherited disorders of haemoglobin. Br. J. Haematol. 149, 635–637 (2010)
Kosaryan, M., Karami, H., Zafari, M., Yaghobi, N.: Report on patients with non transfusion-dependent β-thalassemia major being treated with hydroxyurea attending the Thalassemia Research Center, Sari, Mazandaran Province, Islamic Republic of Iran in 2013. Hemoglobin 38, 115–118 (2014)
Al-Jumeily, D., Hussain, A., Fergus, P.: Using adaptive neural networks to provide self-healing autonomic software. Int. J. Space Based Situated Comput. 5, 129–140 (2015)
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). doi:10.1007/978-3-319-42291-6_78
Al-Jumeily, D., Iram, S., Vialatte, F.-B., Fergus, P., Hussain, A.: A novel method of early diagnosis of Alzheimer’s disease based on EEG signals. Sci. World J. 2015, 11 (2015). Article ID: 931387. http://dx.doi.org/10.1155/2015/931387
Khalaf, M., Hussain, A.J., Keight, R., Al-Jumeily, D., Fergus, P., Keenan, R., Tso, P.: Machine learning approaches to the application of disease modifying therapy for sickle cell using classification models. Neurocomputing 228, 154–164 (2017)
Ionescu, R.T., Popescu, M.: Knowledge Transfer between Computer Vision and Text Mining. Similarity-Based Learning Approaches. ACVPR. Springer, Cham (2016). doi:10.1007/978-3-319-30367-3
Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11, 431–441 (1963)
Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5, 989–993 (1994)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer Science & Business Media, New York (2013)
Al Kafri, A.S., Sudirman, S., Hussain, A.J., Fergus, P., Al-Jumeily, D., Al-Jumaily, M., Al-Askar, H.: A framework on a computer assisted and systematic methodology for detection of chronic lower back pain using artificial intelligence and computer graphics technologies. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2016. LNCS, vol. 9771, pp. 843–854. Springer, Cham (2016). doi:10.1007/978-3-319-42291-6_83
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Khalaf, M. et al. (2017). A Performance Evaluation of Systematic Analysis for Combining Multi-class Models for Sickle Cell Disorder Data Sets. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-63312-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63311-4
Online ISBN: 978-3-319-63312-1
eBook Packages: Computer ScienceComputer Science (R0)