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Background. Headache is the top ranked with 42% percentage of all complaints neurology’s patients.  Focused and systematic approach is needed in making a diagnosis of primary headache type because  management of headache is different for each type. Objective. Enabling users to identify the type of headache. Methods. The experiment was conducted using Naive Bayes classifier method which is the principle is multiplying the percentage likelihood of each variable for each parameter for each class. Results. The percentage value of each parameter obtained from the data of headache patients at neurology polyclinic poly of Dr. Soetomo Hospital within 1 year from the year 2014 to 2015. The percentage value of each class likelihood sought highest value which is the output or decision-diagnosis program. Analysis of each of the input parameters, gender, age, location of head pain, headache characteristics, appeared least autonomous signs, and scale of headache may indicate that each of the options selected by the user influence the decision of the diagnosis program. Conclusion. The design of early detection of primary headaches with the input parameters as mentioned before derived from the raw data as electronic medical records to be analyzed based on methods Naive Bayes classifier resulted in the decision diagnosis of migraine, cluster and TTH have accuracy values by 92 %.

DESIGN OF EXPERT SYSTEM AS A SUPPORT TOOL FOR EARLY DIAGNOSIS OF PRIMARY HEADACHE