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Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varieties of medical methods used to classify cerebral infarction, this study uses a Multiple Support Vector Machine with Information Gain Feature Selection (MSVM-IG). MSVM-IG is a new machine learning method which utilizes the support vector as a new dataset, with feature selection step and performance evaluation performed. Data was obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%. Therefore, MSVM-IG is an alternative method used to assist medical practitioners in classifying cerebral infarction.

Cerebral infarction classification using multiple support vector machine with information gain feature selection