The week's pick
Reseach Article
Classification of Ischemic Stroke using Machine Learning Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 149 - Number 10 |
| Year of Publication: 2016 |
| Authors: Selma Yahiya Adam, Adil Yousif, Mohammed Bakri Bashir |
10.5120/ijca2016911607
|
Selma Yahiya Adam, Adil Yousif, Mohammed Bakri Bashir . Classification of Ischemic Stroke using Machine Learning Algorithms. International Journal of Computer Applications. 149, 10 ( Sep 2016), 26-31. DOI=10.5120/ijca2016911607
Abstract
Stroke is the third leading cause of death following diseases of heart and cancer. The majority of strokes are classified as ischemic which have two types: thrombotic and embolic. In thrombotic stroke, the blood clot (thrombus) forms in one of the arteries that supplies blood to brain. The embolic stroke happens when a blood clot that forms somewhere else in the body (embolus) break loose and travels to the brain through the bloodstream. Hemorrhagic stroke is considered another type of brain stroke by some researchers as it happen when an artery in the brain leaks blood or ruptures. As a reason of hemorrhagic stroke, the brain cells damages as result of the pressure from the leaked blood. There are many similarities between these types and it is difficult to classify the cases accurately using medical procedures. Furthermore, there are no clear boundaries between these types. This paper reviewed and analyzed the current studies on classification of ischemic stroke. Furthermore, the study has developed a classification model for ischemic stroke using decision tree algorithm and k nearest neighbor. The classification model is based on a dataset of 400 cases collected from different Sudanese hospitals. The results of the decision tree algorithm can be used by medical specialist to classify and diagnose ischemic stroke patients. Moreover, the study revealed that some features can be used directly to determine the type of ischemic stroke. These results help the medical doctors in the classification process of ischemic strokes. Furthermore, the results found that most of the ischemic stroke cases in Sudan are thrombotic ischemic stroke.
References
- Ø. Lidegaard, E. Løkkegaard, A. Jensen, C. W. Skovlund, and N. Keiding, "Thrombotic stroke and myocardial infarction with hormonal contraception," New England Journal of Medicine, vol. 366, pp. 2257-2266, 2012.
- Ø. Lidegaard, I. Milsom, R. T. Geirsson, and F. E. Skjeldestad, "Hormonal contraception and venous thromboembolism," Acta obstetricia et gynecologica Scandinavica, vol. 91, pp. 769-778, 2012.
- D. Gierhake, J. Weber, K. Villringer, M. Ebinger, H. Audebert, and J. Fiebach, "[Mobile CT: technical aspects of prehospital stroke imaging before intravenous thrombolysis]," RoFo: Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin, vol. 185, pp. 55-59, 2013.
- S. Payabvash, M. H. Qureshi, S. M. Khan, M. Khan, S. Majidi, S. Pawar, and A. I. Qureshi, "Differentiating intraparenchymal hemorrhage from contrast extravasation on post-procedural noncontrast CT scan in acute ischemic stroke patients undergoing endovascular treatment," Neuroradiology, vol. 56, pp. 737-744, 2014.
- M. G. Lansberg, M. Straka, S. Kemp, M. Mlynash, L. R. Wechsler, T. G. Jovin, M. J. Wilder, H. L. Lutsep, T. J. Czartoski, and R. A. Bernstein, "MRI profile and response to endovascular reperfusion after stroke (DEFUSE 2): a prospective cohort study," The Lancet Neurology, vol. 11, pp. 860-867, 2012.
- J. R. Quinlan, C4. 5: programs for machine learning: Elsevier, 2014.
- B. P. Rimal and E. Choi, "A service‐oriented taxonomical spectrum, cloudy challenges and opportunities of cloud computing," International Journal of Communication Systems, vol. 25, pp. 796-819, 2012.
- M. D. Steenwijk, P. J. Pouwels, M. Daams, J. W. van Dalen, M. W. Caan, E. Richard, F. Barkhof, and H. Vrenken, "Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs)," NeuroImage: Clinical, vol. 3, pp. 462-469, 2013.
- E. C. Jauch, J. L. Saver, H. P. Adams, A. Bruno, B. M. Demaerschalk, P. Khatri, P. W. McMullan, A. I. Qureshi, K. Rosenfield, and P. A. Scott, "Guidelines for the early management of patients with acute ischemic stroke a guideline for healthcare professionals from the American Heart Association/American Stroke Association," Stroke, vol. 44, pp. 870-947, 2013.
- A. Reiss and D. Stricker, "Creating and benchmarking a new dataset for physical activity monitoring," in Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments, 2012, p. 40.
- C. Potter, D. Lew, J. McCaa, S. Cheng, S. Eichelberger, and E. Grimit, "Creating the dataset for the western wind and solar integration study (USA)," Wind Engineering, vol. 32, pp. 325-338, 2008.
- A. Chervenak, I. Foster, C. Kesselman, C. Salisbury, and S. Tuecke, "The data grid: Towards an architecture for the distributed management and analysis of large scientific datasets," Journal of network and computer applications, vol. 23, pp. 187-200, 2000.
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