Abstract
Stroke is a major life-threatening disease mostly occurs to a person of age 65 years and above but nowadays also happen in younger age due to unhealthy diet. If we can predict a stroke in its early stage, then it can be prevented. In this paper, we evaluate five different machine learning techniques to predict stroke on Cardiovascular Health Study (CHS) dataset. We use Decision Tree (DT) with the C4.5 algorithm for feature selection, Principal Component Analysis (PCA) is used for dimension reduction and, Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used for classification. The predictive methods discussed in this paper are tested on different data samples based on different machine learning techniques. From the different methods applied, the composite method of DT, PCA and ANN gives the optimal result.
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References
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1023/A:1022627411411
Zhao, C., Zhang, H., Zhang, X., Liu, M., Hu, Z., Fan, B.: Application of support vector machine (SVM) for prediction of toxic activity of different data sets. Toxicology 217(2), 105–119 (2006). http://www.sciencedirect.com/science/article/pii/S0300483X05004270
Jeena, R.S., Kumar, S.: Stroke prediction using SVM. In: 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, pp. 600–602 (2016). https://doi.org/10.1109/iccicct.2016.7988020
Hssina, B., Merbouha, A., Ezzikouri, H., Erritali, M.: A comparative study of decision tree ID3 and C4.5. Int. J. Adv. Comput. Sci. Appl. (IJACSA) (2014). https://doi.org/10.14569/SpecialIssue.2014.040203. Special Issue on Advances in Vehicular Ad Hoc Networking and Applications
Singh, M.S., Choudhary, P.: Stroke prediction using artificial intelligence. In: 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), August 2017, pp. 158–161 (2017)
Kansadub, T., Thammaboosadee, S., Kiattisin, S., Jalayondeja, C.: Stroke risk prediction model based on demographic data. In: 2015 8th Biomedical Engineering International Conference (BMEiCON), November 2015, pp. 1–3 (2015)
Shanthi, D., Sahoo, D.G., Saravanan, D.N.: Designing an artificial neural network model for the prediction of thrombo-embolic stroke (2004)
Gayathri, P.: Effective analysis and predictive model of stroke disease using classification methods (2012)
Dataset: Cardiovascular Health Study (CHS). https://biolincc.nhlbi.nih.gov/studies/chs/. Accessed 08 May 2016
Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. Roy. Soc. Lond. A Math. Phys. Eng. Sci. 374(2065) (2016). http://rsta.royalsocietypublishing.org/content/374/2065/20150202
Freire, V.A., de Arruda, L.V.R.: Identification of residential load patterns based on neural networks and PCA. In: 2016 12th IEEE International Conference on Industry Applications (INDUSCON), November 2016, pp. 1–6 (2016)
Cilimkovic, M.: Neural networks and back propagation algorithm. Institute of Technology Blanchardstown, Dublin 15, Ireland (2010)
Rojas, R.: Neural Networks - A Systematic Introduction. Springer, Berlin (1996). https://doi.org/10.1007/978-3-642-61068-4
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Singh, M.S., Choudhary, P., Thongam, K. (2020). A Comparative Analysis for Various Stroke Prediction Techniques. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-4018-9_9
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DOI: https://doi.org/10.1007/978-981-15-4018-9_9
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