Manuscript Number : CSEIT2283121

Stroke Risk Prediction Using Machine Learning Algorithms

Authors(5) :-Rishabh Gurjar, Sahana H K, Neelambika C, Sparsha B Sathish, Ramys S

The majority of strokes are brought on by unforeseen obstruction of pathways by the heart and brain. Distinct classifiers have been developed for early detection of different stroke warning symptoms, including Logistics Regression, Decision Tree, KNN, Random Forest, and Naïve Bayes. Furthermore, the proposed research has obtained an accuracy of around 95.4%, with the Random Forest outperforming the other classifiers. This model has the highest stroke prediction accuracy. Therefore, Random Forest is almost the perfect classifier for foretelling stroke, which doctors and patients can utilise to prescribe and identify likely strokes early. Here in our research we have created a website to which model is dumped/loaded, such that the interface will be friendly to the end-users.

Authors and Affiliations

Rishabh Gurjar
Department of Computer Science and Engineering. The National Institute of Engineering, Mysore, Karnataka, India
Sahana H K
Department of Computer Science and Engineering. The National Institute of Engineering, Mysore, Karnataka, India
Neelambika C
Department of Computer Science and Engineering. The National Institute of Engineering, Mysore, Karnataka, India
Sparsha B Sathish
Department of Computer Science and Engineering. The National Institute of Engineering, Mysore, Karnataka, India
Ramys S
Assistant Professor, Department of Computer Science and Engineering. The National Institute of Engineering, Mysore, Karnataka, India

Stroke, Machine Learning, Data Analysis, Normalization, Scalarization, ML Algorithms, Accuracy, Results.

  1. M. Mahmud and colleagues, "A brain-inspired trust management model to provide security in a cloud-based IoT framework for neuroscience applications," Cognitive Computation, vol. 10, no. 5, pp. 864-873, 2018.
  2. "Application of deep learning in diagnosing neurological illnesses from magnetic resonance images: a survey on the identification of Alzheimer's disease, Parkinson's disease, and schizophrenia," Brain Informatics, vol. 7, no. 1, 2020, pp. 1–21.
  3. A. Hussain, M. S. Kaiser, and M. Mahmud, "Deep learning in mining biological data," arXiv preprint arXiv:2003.00108, 2020.
  4. L. Amini, R. Azarpazhouh, M. T. Farzadfar, S. A. Mousavi, F. Jazaieri, F. Khorvash, R. Norouzi, and N. Toghianfar, "Prediction and control of stroke by data mining," International Journal of Preventive Medicine, vol. 4, no. Suppl 2, May 2013, pp. S245-249.
  5. S. F. Sung, C Y Hsieh, Y H Kao Yang, H J Lin, and C H Chen Using data mining methods, it was possible to develop a stroke severity index based on administrative data in November 2015, according to Journal of Clinical Epidemiology, vol. 68, no. 11.
  6. Low cost and portable patient monitoring system for e-health services in Bangladesh, 2016 International Conference on Computer Communication and Informatics (ICCCI), pp. 1-4. M. C. Paul, S. Sarkar, M. M. Rahman, S. M. Reza, and M. S. Kaiser.
  7. "Innovative technique in web application effort and cost estimation utilising functional measurement type," in 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT). IEEE (2015), pages 1–7.
  8. "Toward a heterogeneous mist, fog, and cloud-based framework for the internet of healthcare things," IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4049-4062, 2018. M. Asif-Ur-Rahman, F. Afsana, M. Mahmud, M. S. Kaiser, M.
  9. R. Ahmed, O. Kaiwartya, and A. James-Taylor.
  10. Tamara Islam Meghala, Md., Minazuddin Emon, and Maria Sultana Keya Performance Analysis of Machine Learning Approaches in Stroke Prediction by Mahfujur Rahman in the Fourth International Conference on Electronics, Communication, and Aerospace Technology (ICECA-2020).
  11. Applications of deep learning and reinforcement learning to biological data, IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 6, pp. 2063–2079, 2018. [10] M. Mahmud, M. S. Kaiser, A. Hussain, and S. Vassanelli.
  12. "Classification of stroke disease using machine learning techniques," Neural Computing and Applications, vol. 32, no. 3, Feb. 2020, pp. 817-828. P. Govindarajan, R. K. Soundarapandian, A. H. Gandomi, R. Patan, P. Jayaraman, and R. Manikandan.
  13. In the 2014 International Conference on Electrical Engineering and Information & Communication Technology, S. M. Reza, M. M. Rahman, and S. Al Mamun presented "A novel concept for road networks-a car xml device partnership with big data." IEEE (2014), pages 1–5.

Publication Details

Published in : Volume 8 | Issue 4 | July-August 2022
Date of Publication : 2022-07-05
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 20-25
Manuscript Number : CSEIT2283121
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Rishabh Gurjar, Sahana H K, Neelambika C, Sparsha B Sathish, Ramys S, "Stroke Risk Prediction Using Machine Learning Algorithms", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.20-25, July-August-2022. Available at doi : https://doi.org/10.32628/CSEIT2283121
Journal URL : https://res.ijsrcseit.com/CSEIT2283121 Citation Detection and Elimination     |      |          | BibTeX | RIS | CSV

Article Preview