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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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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