An ant colony optimization algorithm-based classi cation for the diagnosis of primary headaches using a website questionnaire expert system

The purpose of this research was to evaluate the classi cation accuracy of the ant colony optimization algorithm for the diagnosis of primary headaches using a website questionnaire expert system that was completed by patients. This cross-sectional study was conducted in 850 headache patients who randomly applied to hospital from three cities in Turkey with the assistance of a neurologist in each city. The patients lled in a detailed web-based headache questionnaire. Finally, neurologists' diagnosis results were compared with the classi cation results of an ant colony optimization-based classi cation algorithm. The ant colony algorithm for diagnosis classi ed patients with 96.9412% overall accuracy. Diagnosis accuracies of migraine, tension-type, and cluster headaches were 98.2%, 92.4%, and 98.2% respectively. The ant colony optimization-based algorithm has a successful classi cation potential on headache diagnosis. On the other hand, headache diagnosis using a website-based algorithm will be useful for neurologists in order to gather quick and precise results as well as tracking patients for their headache symptoms and medication usage by using electronic records from the Internet.

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