International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 13, No. 2, 8 Apr. 2023
Cover page and Table of Contents: PDF (size: 474KB)
Analyzing the Performance of the Machine Learning Algorithms for Stroke Detection
Full Text (PDF, 474KB), PP.27-35
Views: 0 Downloads: 0
Author(s)
Index Terms
Brain stroke, machine learning, data analysis, prediction
Abstract
A brain stroke is a condition with an insufficient blood supply to the brain, which causes cell death. Due to the lack of blood supply, the brain cells die, and disabilities occurs in different parts of the brain. Strokes have become one of the major causes of death and disability in recent years. Investigating the affected individuals has shown several risk factors that are considered to be causes of stroke. Considering such risk factors, many research works have been performed to classify and predict stroke. In this research, we have applied five machine learning algorithms to identify and classify the stroke from the individual’s medical history and physical activities. Different physiological factors have are considered and applied to machine learning algorithms such as Naïve Bayes, AdaBoost, Decision Table, k-NN, and Random Forest. The algorithm Decision Table performed the best to predict the stroke based on different physiological factors in the applied dataset with an accuracy of 82.1%. The machine learning algorithms can be a helpful for clinical prediction of stroke against individual’s medical history and physical activities in a better way.
Cite This Paper
Trailokya Raj Ojha, Ashish Kumar Jha, "Analyzing the Performance of the Machine Learning Algorithms for Stroke Detection ", International Journal of Education and Management Engineering (IJEME), Vol.13, No.2, pp. 27-35, 2023. DOI:10.5815/ijeme.2023.02.04
Reference
[1]“World Stroke Organization,” Available: https://www.world-stroke.org/world-stroke-day-campaign/why-stroke-matters/learnabout-stroke . [Accessed: August 10, 2022]
[2]T. R. Dawber, G. F. Meadors, and F. E. Moore, “Epidemiological Approaches to Heart Disease: The Framingham Study*.”
[3]P. A. Wolf, R. B. D, A. J. Belanger, and W. B. Kannel, “Probability of Stroke: A Risk Profile From the Framingham Study.” [Online]. Available: http://ahajournals.org
[4]M. S. Donaldson, J. M. Corrigan, and L. T. Kohn, “To err is human: building a safer health system,” National academy press Washington, DC, vol. 6, 2000.
[5]“Brain stroke prediction dataset,” https://www.kaggle.com/datasets/zzettrkalpakbal/full-filled-brain-stroke-dataset.
[6]“Concept of stroke by healthline,” https://www.cdc.gov/stroke/index.htm. [Accessed: August 12, 2022]
[7]“Statistics of stroke by Centers for disease control and prevention,” https://www.cdc.gov/stroke/facts.htm. [Accessed: August 12, 2022]
[8]P. Govindarajan, R. K. Soundarapandian, A. H. Gandomi, R. Patan, P. Jayaraman, and R. Manikandan, “Classification of stroke disease using machine learning algorithms,” Neural Computing & Applications, vol. 32, no. 3, pp. 817–828, 2020.
[9]L. Amini, R. Azarpazhouh, and M. T. Farzadfar, “Prediction and control of stroke by data mining,” International Journal of Preventive Medicine, vol. 4, no. 2, pp. S245–S249, 2013.
[10]C. A. Cheng, Y. C. Lin, and H. W. Chiu, “Prediction of the prognosis of ischemic stroke patients after intravenous thrombolysis using artificial neural networks,” Studies in Health Technology and Informatics, vol. 202, pp. 115–118, 2014.
[11]S. Cheon, J. Kim, and J. Lim, “The use of deep learning to predict stroke patient mortality,” International Journal of Environmental Research and Public Health, vol. 16, no. 11, 2019.
[12]M. S. Singh and P. Choudhary, “Stroke prediction using artificial intelligence,” in Proceedings of the 2017 8th Annual Industrial Automation And Electromechanical Engineering Conference (IEMECON), Aug. 2017, pp. 158–161.
[13]C.-L. Chin, B.-J. Lin, and G.-R. Wu et al., “An automated early ischemic stroke detection system using CNN deep learning algorithm,” in Proceedings of the 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), Nov. 2017, pp. 368–372.
[14]S.-F. Sung, C.-Y. Hsieh, and Y.-H. Kao Yang et al., “Developing a stroke severity index based on administrative data was feasible using data mining techniques,” Journal of Clinical Epidemiology, vol. 68, no. 11, pp. 1292–1300, 2015.
[15]M. Monteiro, A. C. Fonseca, and A. T. Freitas et al., “Using machine learning to improve the prediction of functional outcome in ischemic stroke patients,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 15, no. 6, pp. 1953–1959, 2018.
[16]S. Y. Adam, A. Yousif, and M. B. Bashir, “Classification of ischemic stroke using machine learning algorithms,” International Journal of Computer Application, vol. 149, no. 10, pp. 26–31, 2016.
[17]T. Tazin, M. N. Alam, N. N. Dola, M. S. Bari, S. Bourouis, and M. Monirujjaman Khan, “Stroke Disease Detection and Prediction Using Robust Learning Approaches,” Journal of Healthcare Engineering, vol. 2021, 2021, doi: 10.1155/2021/7633381.
[18]T. I. Shoily, T. Islam, S. Jannat, S. A. Tanna, T. M. Alif, and R. R. Ema, “Detection of stroke disease using machine learning algorithms,” in In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE., 2019, pp. 1–6.
[19]D.-C. Feng et al., “Machine learning-based compressive strength prediction for concrete: an adaptive boosting approach.”
[20]G. Wets, J. Vanthienen, and S. Piramuthu, “Extending a tabular knowledge-based framework with feature selection,” Expert Systems with Applications, vol. 13, no. 2, pp. 109–119, 1997.
[21]J. Vanthienen and E. Dries, “Illustration of a decision table tool for specifying and implementing knowledge based systems,” International Journal on Artificial Intelligence Tools, vol. 3, no. 2, pp. 267–288, 1994.
[22]G. Sailasya and G. L. Aruna Kumari, “Analyzing the Performance of Stroke Prediction using ML Classification Algorithms.” [Online]. Available: www.ijacsa.thesai.org
[23]A. Pandey and A. Jain, “Comparative Analysis of KNN Algorithm using Various Normalization Techniques,” International Journal of Computer Network and Information Security, vol. 9, no. 11, pp. 36–42, Nov. 2017, doi: 10.5815/ijcnis.2017.11.04.
[24]“WEKA Tool,” Available Online: https://www.weka.io/. [Accessed: August 8, 2022]