A Predictive Model of Stroke Diseases using Machine Learning Techniques
Alaa Ghannam1, Jaber Alwidian2

1Alaa Ghannam*, MBA Student, Talal Abu Ghazaleh University Collage for Innovations (TAGUCI), Amman, Jordan. 
2Jaber Alwidian, Assistant Professor, Department of Data Science and Artificial Intelligence, University of Petra (UOP), Amman, Jordan.
Manuscript received on 21 March 2022. | Revised Manuscript received on 05 April 2022. | Manuscript published on 30 May 2022. | PP: 53-59 | Volume-11 Issue-1, May 2022. | Retrieval Number: 100.1/ijrte.A69000511122 | DOI: 10.35940/ijrte.A6900.0511122
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Due to rapid changing in human lifestyles, a set of biological factors of human lives has changed, making people more vulnerable to certain diseases such as stroke. Stroke is a life-threatening disease leading to a long-term disability. It’s now a leading cause of death all over the word. As well as it’s the second leading cause of death after ischemic heart disease in Jordan. Stroke detection within the first few hours improves the chances to prevent complications and improve health care and management of patients. In this study we used patient’s information that are believed to be related to the cause of stroke and applied machine learning techniques such as Naive Bayes, Decision Tree, and KNN to predict stroke. Orange software is used to automatically process data and generate data mining model that can be used by health care professionals to predict stroke disease and give better treatment plan. Results show that decision tree classifier outperformed other techniques with accuracy level of 94.2%. 
Keywords: Data Mining, Classification, Stroke, Healthcare, Machine Learning.
Scope of the Article: Machine Learning