Abstract: Stroke is a neurological disease that occurs when a brain cells die as a result of oxygen and nutrient deficiency. Stroke detection within the first few hours improves the chances to prevent complications and improve health care and management of patients. In addition, significant effect of medications that were used as treatment for stroke would appear only if they were given within the first three hours since the beginning of stroke. A framework has been designed based on data mining techniques on Stroke data set that is obtained from Ministry of National Guards Health Affairs hospitals, Kingdom of Saudi Arabia. A data mining model was built with 95% accuracy. Furthermore, this study showed that patient with the following medical conditions, such as heart diseases (hypertension mainly), immunity diseases, diabetes militias, kidney diseases, hyperlipidemia, epilepsy, or blood (platelets) disorders has a higher probability to develop stroke.

Ohoud Almadani and Riyad Alshammari, “Prediction of Stroke using Data Mining Classification Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 9(1), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090163

@article{Almadani2018,
title = {Prediction of Stroke using Data Mining Classification Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090163},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090163},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {1},
author = {Ohoud Almadani and Riyad Alshammari}
}