Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques

Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques

##plugins.themes.bootstrap3.article.sidebar##

   PDF
Submitted Nov 15, 2022
Published Jan 20, 2023
DOI https://doi.org/10.24018/ejece.2023.7.1.483
Abstract viewed = 48 times
Issue
Vol. 7 No. 1 (2023)
Section
Articles

##plugins.themes.bootstrap3.article.main##

  •   Senjuti Rahman

    •    Ahsanullah University of Science & Engineering, Bangladesh
  •   Mehedi Hasan

    •    RUET, Bangladesh
  •   Ajay Krishno Sarkar

    •    RUET, Bangladesh

Abstract

The brain is the human body's primary upper organ. Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. Stroke is considered as medical urgent situation and can cause long-term neurological damage, complications and often death. The World Health Organization (WHO) claims that stroke is the leading cause of death and disability worldwide. Early detection of the numerous stroke warning symptoms can lessen the stroke's severity. The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. To gauge the effectiveness of the algorithm, a reliable dataset for stroke prediction was taken from the Kaggle website. Several classification models, including Extreme Gradient Boosting (XGBoost), Ada Boost, Light Gradient Boosting Machine, Random Forest, Decision Tree, Logistic Regression, K Neighbors, SVM - Linear Kernel, Naive Bayes, and deep neural networks (3-layer and 4-layer ANN) were successfully used in this study for classification tasks. The Random Forest classifier has 99% classification accuracy, which was the highest (among the machine learning classifiers). The three layer deep neural network (4-Layer ANN) has produced a higher accuracy of 92.39% than the three-layer ANN method utilizing the selected features as input. The research's findings showed that machine learning techniques outperformed deep neural networks.

Keywords: Deep Neural Network, Extreme Gradient Boosting, Machine Learning, Stroke Prediction.

References

Pikula A, Howard BV, Seshadri S. Stroke and Diabetes. In: Cowie CC, Casagrande SS, Menke A, et al., editors. Diabetes in America. (3rd ed.). Bethesda (MD): National Institute of Diabetes and Digestive and Kidney Diseases (US), 2018, ch.19.

Gary H, Gibbons L. National Heart, Lung and Blood Institute. 2022 [updated 2022 March 24]. Available from: https://www.nhlbi.nih.gov/health/stroke.

Jeena RS, Kumar S. Stroke prediction using SVM, International Conference on Control. Instrumentation, Communication and Computational Technologies (ICCICCT), 2016: 600–602.

Hanifa SM, Raja SK. Stroke risk prediction through non-linear support vector classification models. Int. J. Adv. Res. Comput. Sci., 2010; 1(3).

Chantamit-o P, Madhu G. Prediction of Stroke Using Deep Learning Model. International Conference on Neural Information Processing, 2017: 774-781.

Khosla A, Cao Y, Lin CCY, Chiu HK, Hu J, Lee H. An integrated machine learning approach to stroke prediction, in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010: 183–192.

Hung CY, Lin CH, Lan TH, Peng GS, Lee CC. Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database. PLOS ONE, 2019;14(3):e0213007. https://doi.org/10.1371/journal.pone.0213007.

Adam SY, Yousif A, Bashir MB. Classification of ischemic stroke using machine learning algorithms. International Journal of Computer Application, 2016;149(10):26–31.

Singh MS, Choudhary P. Stroke prediction using artificial intelligence. 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), 2017:158–161.

Emon MU, Keya MS, Meghla TI, Rahman MA, Mamun SA, Kaiser MS. Performance Analysis of Machine Learning Approaches in Stroke Prediction, International Conference on Enumerative Combinatorics and Applications, Nov. 2021.

Kansadub T, Ammaboosadee S, Kiattisin S, Jalayondeja C. Stroke risk prediction model based on de-mographic data, in Proceedings of the 2015 8th Biomedical Engineering International Conference (BMEiCON), Pattaya, -Thailand, November 2015: 1-3.

Tazin T, Alam MN, Dola NN, Bari MS, Bourouis S, Khan M. Stroke Disease Detection and Prediction Using Robust Learning Approaches. Journal of Healthcare Engineering, 2021:1-12. doi: 10.1155/2021/7633381.

Sharma C, Sharma S, Kumar M, Sodhi A. Early Stroke Prediction Using Machine Learning. International Conference on Decision Aid Sciences and Applications; Mar. 2022.

Teoh D. Towards stroke prediction using electronic health records. BMC Medical Informatics and Decision Making, 2018; Dec.(1): 1–11. doi: 10.1186/s12911-018-0702-y.

Hung CY, Lin CH, Lan TH, Peng GS, Lee CC. Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2017: 3110–3113.

Fang G, Huang Z, Wang Z. Predicting Ischemic Stroke Outcome Using Deep Learning Approaches. Front Genet. 2022 Jan 24;12:827522. doi: 10.3389/fgene.2021.827522.

Safavian SR, Landgrebe D. A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 1991 May-June; 21(3): 660-674. doi: 10.1109/21.97458.

Navada A, Ansari AN, Patil S, Sonkamble BA. Overview of use of decision tree algorithms in machine learning. IEEE Control and System Graduate Research Colloquium, ICSGRC, 2011: 37–42.

Rahman MM, Rana MR, Alam NAA, Khan MSI. A web-based heart disease prediction system using machine learning algorithms; 2022 June; 12. 64-80.

Dhillon S, Bansal C, Sidhu B. Machine Learning Based Approach Using XGboost for Heart Stroke Prediction. in International Conference on Emerging Technologies: AI, IoT, and CPS for Science & Technology Applications, September 06–07, 2021.

Akash K, Shashank HN, Srikanth S, Thejas AM. Prediction of Stroke Using Machine Learning. June 2020.

Aiello S, Cliff C, Roark H, Rehak L, Stetsenko P, and Bartz A. Machine Learning with Python and H2O. (5th Ed.). H2O. ai Inc. Nov. 2017.

Sailasya G and Kumari G. L. A. Analyzing the performance of stroke prediction using ML classification algorithms. International Journal of Advanced Computer Science And Applications. 2021; 12(6): 539–545.

Gurjar R, Sahana K, Sathish BS. Stroke Risk Prediction Using Machine Learning Algorithms. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022: 20-25. doi: 10.32628/CSEIT2283121.

Tavares J-A. Stroke prediction through Data Science and Machine Learning Algorithms. 2021; doi: 10.13140/RG.2.2.33027.43040.

Martín J.R, Ayala J.L, Roselló G.R and Camarasaltas J. M. Comparison of Different Machine Learning Approaches to Model Stroke Subtype Classification and Risk Prediction. Spring Simulation Conference (SpringSim), pp. 1-10, 2019.

Downloads

Download data is not yet available.

##plugins.themes.bootstrap3.article.details##

How to Cite
[1]
Rahman, S., Hasan, M. and Sarkar, A.K. 2023. Prediction of Brain Stroke using Machine Learning Algorithms and Deep Neural Network Techniques. European Journal of Electrical Engineering and Computer Science. 7, 1 (Jan. 2023), 23–30. DOI:https://doi.org/10.24018/ejece.2023.7.1.483.
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.