Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation

Main Article Content

Fadratul Hafinaz Hassan
https://orcid.org/0000-0002-5549-6379
Mohd Adib Omar
https://orcid.org/0000-0002-6886-8530

Abstract

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning algorithms implementation in the recurrent stroke prediction models. This research aims to investigate and compare the performance of machine learning algorithms using recurrent stroke clinical public datasets. In this study, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Bayesian Rule List (BRL) are used and compared their performance in the domain of recurrent stroke prediction model. The result of the empirical experiments shows that ANN scores the highest accuracy at 80.00%, follows by BRL with 75.91% and SVM with 60.45%.

Downloads

Download data is not yet available.

Article Details

How to Cite
1.
Hassan FH, Omar MA. Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation. Baghdad Sci.J [Internet]. 2021 Dec. 20 [cited 2022 Nov. 30];18(4(Suppl.):1406. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6641
Section
article

References

Guide- Miss. B. M. Gund, Mrs. P. N. Jagtap, Mr. V. B. Ingale, and Dr. R. Y. Patil, "Stroke: A Brain Attack," IOSR Journal of Pharmacy, vol. 3, pp. 1-23, 2013.

American Heart Association. (2015, August 30). Prevention and Treatment of Diabetes. Available: https://www.heart.org/en/health-topics/diabetes/prevention--treatment-of-diabetes

R. Murugappan, "Protecting Against Stroke," in The Star, ed. Malaysia: Star Media Group Berhad 2017.

J. Burn, M. Dennis, J. Bamford, P. Sandercock, D. Wade, and C. Warlow, "Long-term risk of recurrent stroke after a first-ever stroke. The Oxfordshire Community Stroke Project," Stroke, vol. 25, pp. 333-337, 1994.

M. Awad and R. Khanna, Efficient learning machines: theories, concepts, and applications for engineers and system designers: Apress, 2015.

J. D. Spence, "Recent advances in preventing stroke recurrence," F1000Research, vol. 6, 2017.

H. Asadi, R. Dowling, B. Yan, and P. Mitchell, "Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy," PloS one, vol. 9, p. e88225, 2014.

Arslan, A.K., Colak, C. and Sarihan, M.E., “Different medical data mining approaches based prediction of ischemic stroke,” Computer methods and programs in biomedicine, 130, pp.87-92, 2016.

Jeena, R.S. and Kumar, S., “Stroke prediction using SVM”. In 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (pp. 600-602). IEEE, 2016

Hung, C.Y., Chen, W.C., Lai, P.T., Lin, C.H. and Lee, C.C., “Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database”, In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3110-3113). IEEE, 2017.

Weng, S.F., Reps, J., Kai, J., Garibaldi, J.M. and Qureshi, N.,“Can machine-learning improve cardiovascular risk prediction using routine clinical data?”, PloS one, 12(4), p.e0174944, 2017.

Monteiro, M., Fonseca, A.C., Freitas, A.T., e Melo, T.P., Francisco, A.P., Ferro, J.M. and Oliveira, A.L., “Using machine learning to improve the prediction of functional outcome in ischemic stroke patients”, IEEE/ACM transactions on computational biology and bioinformatics, 15(6), pp.1953-1959, 2018.

Asadi, H., Dowling, R., Yan, B. and Mitchell, P., “Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy”, PloS one, 9(2), p.e88225, 2014.

Colak, C., Karaman, E. and Turtay, M.G., “Application of knowledge discovery process on the prediction of stroke. Computer methods and programs in biomedicine”, 119(3), pp.181-185, 2015

Purusothaman, G. and Krishnakumari, P., “A survey of data mining techniques on risk prediction: Heart disease”, Indian Journal of Science and Technology, 8(12), p.1., 2015.

Sung, S.F., Hsieh, C.Y., Yang, Y.H.K., Lin, H.J., Chen, C.H., Chen, Y.W. and Hu, Y.H., “Developing a stroke severity index based on administrative data was feasible using data mining techniques”, Journal of clinical epidemiology, 68(11), pp.1292-1300, 2015.

Kirasich, K., Smith, T. and Sadler, B., “Random forest vs logistic regression: binary classification for heterogeneous datasets”. SMU Data Science Review, 1(3), p.9, 2018.

Okut, H., “Bayesian regularized neural networks for small n big p data”, Artificial neural networks-models and applications, pp.28-48, 2016.

Kim, J. and Canny, J., “Explainable Deep Driving by Visualizing Causal Attention”, In Explainable and Interpretable Models in Computer Vision and Machine Learning (pp. 173-193). Springer, Cham, 2018.