ANALYSIS OF STROKE PREDICTION USING DEEP LEARNING

Authors

  • S.Kavitha Bharathi Assistant Professor,Department of Computer Applications,Kongu Engineering College,Perundurai, Tamilnadu, India, Author
  • M. Dhavamani Assistant Professor,Department of Mathematics,Kongu Engineering College,Perundurai, Tamilnadu, India, +91 984274601 Author
  • P.Saravana Raj PG Scholar,Department of Computer Applications,Kongu Engineering College,Perundurai, Tamilnadu, India,, +91 8531028433. Author

DOI:

https://doi.org/10.61841/5m3ny244

Keywords:

stroke, forecast, deep learning, include extraction.

Abstract

Early finding of stroke is basic for opportune avoidance and treatment. It is the significant purpose behind death, because of clusters and breaks in the veins, causing the cerebrum tissues to harm. The clinical office is constantly inquisitive about finding a superior path for anticipating stroke. The recuperation of stroke patients is an exceptionally moderate procedure and furthermore over the top expensive. As an answer for lessen recuperation time term and forestall malady. A framework gives a superior method to foreseeing stroke and it would be extraordinary. In this paper we utilized a few techniques and calculations to foresee stroke. The strategies are Deep Neural Network approaches and Principal part examination technique. We contrasted our strategy with five other AI strategies are Dimensionality decrease, Ensemble techniques, Regression, Reinforcement learning, Anomaly Detection. These strategies are successful not exactly our techniques. The Deep Neural Network approaches and key part investigation technique are the most proficient strategy to predict stroke.

 

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References

1. Pastore, D.; Pacifici, F.; Capuani, B.; Palmirotta, R.; Dong, C.; Coppola, A.; Abete, P.; Roselli, M.; Sbraccia, P.; Guadagni, F.; et al. Sex-genetic interaction in the risk for cerebrovascular disease. Curr. Med. Chem. 2017, 24, 2687–2699. [CrossRef]

2. Kim, H.C.; Choi, D.P.; Ahn, S.V.; Nam, C.M.; Suh, I. Six-year survival and causes of death among stroke patients in Korea. Neuroepidemiology 2009, 32, 94–100. [CrossRef] [PubMed]

3. Lee, H.; Oh, S.H.; Cho, H.; Cho, H.J.; Kang, H.Y. Prevalence and socio-economic burden of heart failure in an aging society of South Korea. BMC Cardiovasc. Disord. 2016, 16, 215.

4. Lee, A.H.; Yau, K.K.; Wang, K. Recurrent ischaemic stroke hospitalisations: A retrospective cohort study

5. using Western Australia linked patient records. Eur. J. Epidemiol. 2004, 19, 999–

6. 1003. [CrossRef] [PubMed]

7. Chuang, K.Y.; Wu, S.C.; Ma, A.H.; Chen, Y.H.; Wu, C.L. Identifying factors associated with hospital readmissions among stroke patients in Taipei. J. Nurs. Res. 2005, [CrossRef] [PubMed]

8. Joo, H.; George, M.G.; Fang, J.; Wang, G. A literature review of indirect costs associated with stroke. J. Stroke Cerebrovasc. Dis. 2014.

9. Andersen, H.E.; Schultz-Larsen, K.; Kreiner, S.; Forchhammer, B.H.; Eriksen, K.; Brown, A. Can readmission after stroke be prevented? Results of a randomized clinical study: A postdischarge follow-up service for stroke survivors.

10. Brainin, M.; Bornstein, N.; Boysen, G.; Demarin, V. Acute neurological stroke care in Europe: Results of the European Stroke Care Inventory. Eur. J. Neurol. 2000.

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Published

31.10.2020

How to Cite

Bharathi, S., Dhavamani, M., & Raj, P. (2020). ANALYSIS OF STROKE PREDICTION USING DEEP LEARNING. International Journal of Psychosocial Rehabilitation, 24(8), 10291-10296. https://doi.org/10.61841/5m3ny244