Naïve bayes classifier models for cerebral infarction classification

Cerebral infarction is a condition of tissue damage in the brain caused by inadequate oxygen supply caused by obstruction of the flow of regions to the area (ischemia). Brain ischemia is more common than hemorrhagic, but surgery is most often performed in hemorrhagic strokes. This condition is also called stroke infarction. In stroke infarction does not occur bleeding. Changes in brain blood vessel walls can be primary due to congenital or degenerative abnormalities and secondary processes caused by other processes such as inflammation, arteriosclerosis, hypertension, diabetes mellitus and many other processes, so the cause of stroke is very multifactorial. The final sign of cell damage due to ischemia is marked by the nucleus which becomes picnotic and fragmented. Research shows that stroke infarction can occur at the age of 15-55 years. To diagnose the presence or absence of cerebral infarction in the brain is not enough just to use a CT scan, therefore machine learning will also be used to diagnose the presence or absence of cerebral infarction in the brain. For this reason, the authors propose the Naïve Bayes Classifier method as a classification method that has good accuracy, good precision, good memory, and a good F1-score in calcifying a patient whose brain has cerebral infarction or not. In this proposed method, Naïve Bayes Classifier is a probabilistic machine learning model used to classify. Naïve Bayes Classifier is a simple probability technique based on the Bayes theorem with the assumption of independence among predictors. In simple terms, Naïve Bayes Classifier assumes that the presence of certain features in a class is not related to the presence of other features. This method can achieve an accuracy value of up to 92.43%, so this method can be an efficient classification tool.