A Performance Evaluation of Systematic Analysis for Combining Multi-class Models for Sickle Cell Disorder Data Sets
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.
KeywordsMachine-learning classifiers Sickle cell disorder SCD date sets Accuracy Performance evaluation
- 2.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)CrossRefGoogle Scholar
- 4.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 CrossRefGoogle Scholar
- 5.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
- 12.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 CrossRefGoogle Scholar