(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 5, 2020 A High Performance System for the Diagnosis of Headache via Hybrid Machine Learning Model Ahmad Qawasmeh 1 Dept. of Computer Science The Hashemite University Zarqa, Jordan Noor Alhusan 2 , Feras Hanandeh 3 Dept. of Computer Information System The Hashemite University Zarqa, Jordan Maram Al-Atiyat 4 Department of Family Medicine Jordan University of Science and Technology, Jordan Abstract—Headache has been a major concern for patients, medical doctors, clinics and hospitals over the years due to several factors. Headache is categorized into two major types: (1) Primary Headache, which can be tension, cluster or migraine, and (2) Secondary Headache where further medical evaluation must be considered. This work presents a high performance Headache Prediction Support System (HPSS). HPSS provides preliminary guidance for patients, medical students and even clinicians for initial headache diagnosis. The mechanism of HPSS is based on a hybrid machine learning model. First, 19 selected attributes (questions) were chosen carefully by medical specialists according to the most recent International Classification of Headache Disorders (ICHD-3) criteria. Then, a questionnaire was prepared to confidentially collect data from real patients under the supervision of specialized clinicians at different hospitals in Jordan. Later, a hybrid solution consisting of clustering and classification was employed to emphasize the diagnosis results obtained by clinicians and to predict headache type for new patients respectively. Twenty-six (26) different classification algorithms were applied on 614 patients’ records. The highest accuracy was obtained by integrating K-Means and Random Forest with a migraine accuracy of 99.1% and an overall accuracy of 93%. Our web-based interface was developed over the hybrid model to enable patients and clinicians to use our system in the most convenient way. This work provides a comparative study of different headache diagnosis systems via 9 different performance metrics. Our hybrid model shows a great potential for highly accurate headache prediction. HPSS was used by different patients, medical students, and clinicians with a very positive feedback. This work evaluates and ranks the impact of headache symptoms on headache diagnosis from a machine learning perspective. This can help medical experts for further headache criteria improvements. Keywords—High performance computing; Clinical Decision Support System (CDSS); machine learning; primary and secondary headache; performance analysis and improvement; headache diag- nosis; open medical application I. I NTRODUCTION Headache is a common community physical discomfort, which has a negative impact on people’s life especially in terms of work productivity and social relations. Headache is a pain in the various parts of head, which is categorized into two major types: primary headache and secondary headache. Primary headache consists of three main types: • Tension-Type headache (TTH) [1]: it is a very com- mon, mild to moderate head pain, which often feels like a tight band around the head but it can also be intense. Its causes are not understood very well. • Cluster headache [2]: it is one of the most painful headache types. Patients may wake up at night because of the intensity in one side of head and/or around one eye. • Migraine headache [3]: it is a recurring severe headache, which usually affects one side of the head accompanied with nausea, visual disturbances, and sound and light intolerance. In contrast, secondary headache [4] is caused by or oc- curred secondarily to a long list of other conditions. The most common of which is medication-overuse headache. This type requires further medical examinations for better diagnosis. Patients can buy over-the-counter headache pain medicines, which might be harmful in some cases. On the other hand, the increase of the numbers of patients put significant pressure on clinicians and healthcare facilities. This pressure may lead to unexpected medical errors. There is an increased risk of depression that may affect patients suffering from severe headaches. Moreover, some types of headache may cause silent death under certain circumstances because of different reasons such as lack of healthcare, wrong diagnosis, or getting large doses of painkillers. Regrettably, the process of headache diagnosis is not trivial because of the similarity in all headache types’ symptoms. The short time spent by doctors in hospitals on each patient’s case may arise medical errors [5] because of the large number of visiting patients. According to [6], [7], headache is one of the main reasons for medical consultation in the primary care units and neurological clinics. Furthermore, non-specialist people cannot detect the difference between headache symptoms, which could put their lives at risk. In some cases, such as headaches caused by high blood pressure or low blood sugar, painkiller is useless. A big challenge facing the healthcare industry is the quality of service. Quality of service means diagnosing the diseases correctly while providing effective treatment to patients. Celik et al. [5] showed that poor diagnosis can lead to disastrous unacceptable consequences. Therefore, it is essential for research scientists to move towards more efficient diagnosis methods for the sake of better healthcare. It is necessary to find solutions to reduce the bad consequences of headache by increasing patient’s awareness of his health. The revolution of computer-based systems and technolo- gies has led to the development of decision support systems www.ijacsa.thesai.org 655 | Page