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

The purpose of this research was to evaluate the classification 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 filled in a detailed web-based headache questionnaire. Finally, neurologists' diagnosis results were compared with the classification results of an ant colony optimization-based classification algorithm. The ant colony algorithm for diagnosis classified 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 classification 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

An application of a hybrid intelligent system for diagnosing primary headaches

[Abstract] (1) Background: Modern medicine generates a great deal of information that stored in medical databases. Simultaneously, extracting useful knowledge and making scientific decisions for diagnosis and treatment of diseases becomes increasingly necessary. Headache disorders are the most prevalent of all the neurological conditions. Headaches have not only medical but also great socioeconomic significance. The aim of this research is to develop an intelligent system for diagnosing primary headache disorders. (2) Methods: This research applied various mathematical, statistical and artificial intelligence techniques, among which the most important are: Calinski-Harabasz index, Analytical Hierarchy Process, and Weighted Fuzzy C-means Clustering Algorithm. These methods, techniques and methodologies are used to create a hybrid intelligent system for diagnosing primary headache disorders. The proposed intelligent diagnostic system is tested with original real-world data set with different metrics. (3) Results: First at all, nine of 20 attributes – features from International Headache Society (IHS) criteria are selected, and then only five most important attributes from IHS criteria are selected. The calculation result based on the Calinski–Harabasz index value (178) for the optimal number of clusters is three, and they present three classes of headaches: (i) migraine, (ii) tension-type headaches (TTHs), and (iii) other primary headaches (OPHs). The proposed hybrid intelligent system shows the following quality metrics: Accuracy 75%; Precision 67% for migraine, 74% for TTHs, 86% for OPHs, and Average Precision 77%; Recall 86% for migraine, 73% for TTHs, 67% for OPHs, Average Recall 75%; F1 score 75% for migraine, 74% for TTHs, 75% for OPHs, and Average F1 score 75%. (4) Conclusions: The hybrid intelligent system presents qualitative and respectable experimental results. The implementation of existing diagnostics systems and the development of new diagnostics systems in medicine is necessary in order to help physicians make quality diagnosis and decide the best treatments for the patients.Ministerio de Ciencia e Innovación; MINECO-TIN2017-84804-RGobierno del Principado de Asturias; FCGRUPIN-IDI/2018/000226Serbia. Ministry of Education, Science and Technological Development; 451-03-68/2020-14/20015

Baş ağrısı teşhisi için bir karar destek sisteminin geliştirilmesi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Baş ağrısı yaşam kalitesini olumsuz yönde etkileyen yaygın bir sağlık sorunudur. Çoğunlukla selim bir ağrı türüdür ve zamanla kendiliğinden geçer. Ancak herhangi bir etiyolojiye dayanmayan başlı başına hastalık olan türleri mevcuttur. Bu türler uzman bir nöroloji doktoru eşliğinde tedavi gerektirir. Baş ağrılarının teşhisi ve tedavisi için Uluslararası Baş Ağrısı Derneği tarafından yayınlanan sınıflama kriterleri kullanılır. Bu kriterler, birbirlerine benzeyen ve birbirleri ile şartlı ilişkilere sahip, karmaşık birçok maddeden oluşmaktadır. Kriterlerin doğru kullanılması ve hastanın düzenli şekilde takip edilmesiyle en doğru teşhis konulabilir ve uygun tedavi yapılabilir. Bu tezde, baş ağrılarını doğru bir şekilde teşhis edebilmek ve hastalığın ilerleyişini kontrol etmek amacıyla bilgisayar destekli bir takip ve kriterlere göre bir sınıflama sisteminin geliştirilmesi anlatılmıştır. Bu sistem kullanılarak toplanan hasta kayıtları ile veri madenciliği yapılmış ve farklı algoritmalar doğruluk, hassasiyet ve kesinlik açısından karşılaştırılmıştır. Sistem web tabanlı olarak geliştirilmiş olup, kural tabanlı bir algoritma ile baş ağrısı teşhis kriterlerine göre sınıflama yapmaktadır. Ayrıca sistemde bir takvim modülü hazırlanmış olup, hastaların baş ağrısı ve aura atakları yanı sıra ilaç kullanımları da aylık, haftalık ve günlük olarak bu modül yardımı ile takip edilebilmektedir. Türkiye'nin üç farklı şehrinden elde edilen 850 hasta kaydı ile yapay bağışıklık sistemi algoritmaları, karınca koloni algoritması ve yapay arı koloni algoritmasının sınıflandırma performansları gözlemlenmiştir. Ancak kural tabanlı sınıflama algoritması baş ağrılarının alt kümelerini de teşhis ederken, yapay zekâ algoritmaları eğitim verilerinin çeşitliğinin az olması sebebiyle sadece ana gruptaki baş ağrılarını sınıflandırabilmektedir. Alt kümeler sınıflandırıldığı zaman doğruluk oranı azalmaktadır.Headache is a common health problem which negatively affects life quality. Mostly, it is a benign pain and it disappears in time without treatment. However, there are some headache types which are diseases on their own that do not have an underlying etiology. Thus, they require medical treatment with guidance of a neurologist. The criteria published by International Headache Society are used for headache diagnosis and treatment. These criteria involve several complex criterion which are similar and related to each other with several conditions. The most precise diagnosis result can be reached by patients' regular follow-up and the right use of criteria. In this thesis, the development of a classification system based on the criteria and computer-aided patient follow-up in order to control the progress of disease for correct diagnosis of headaches was explained. Data mining was used with the gathered patients' records by using this system and different algorithms are compared in terms of accuracy, sensitivity and specificity. The system was developed as web-based and it makes classification according to the headache diagnosis criteria through the use of the rule based algorithm. Additionally, a calendar module was prepared and the patients' daily, weekly and monthly headache and aura attacks as well as the medication usage could be followed with the help of this module. The classification performance of the artificial immune system algorithms, the ant colony algorithm and the artificial bee colony algorithm were evaluated with the use of 850 patients' records from three different cities of Turkey. Although the rule based classification algorithm was able to classify all the sub-groups of the main types of headaches, the artificial intelligence algorithms could only classify the main types of headaches due to the lack of diversity in training data. Accuracy value decreased when the sub-groups of headaches were classified

Intelligent Systems Approach for Classification and Management of Patients with Headache

Primary headache disorders are the most common complaints worldwide. The socioeconomic and personal impact of headache disorders is enormous, as it is the leading cause of workplace absence. Headache patients’ consultations are increasing as the population has increased in size, live longer and many people have multiple conditions, however, access to specialist services across the UK is currently inequitable because the numbers of trained consultant neurologists in the UK are 10 times lower than other European countries. Additionally, more than two third of headache cases presented to primary care were labelled with unspecified headache. Therefore, an alternative pathway to diagnose and manage patients with primary headache could be crucial to reducing the need for specialist assessment and increase capacity within the current service model. Several recent studies have targeted this issue through the development of clinical decision support systems, which can help non-specialist doctors and general practitioners to diagnose patients with primary headache disorders in primary clinics. However, the majority of these studies were following a rule-based system style, in which the rules were summarised and expressed by a computer engineer. This style carries many downsides, and we will discuss them later on in this dissertation. In this study, we are adopting a completely different approach. The use of machine learning is recruited for the classification of primary headache disorders, for which a dataset of 832 records of patients with primary headaches was considered, originating from three medical centres located in Turkey. Three main types of primary headaches were derived from the data set including Tension Type Headache in both episodic and chronic forms, Migraine with and without Aura, followed by Trigeminal Autonomic Cephalalgia that further subdivided into Cluster headache, paroxysmal hemicrania and short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing. Six popular machine-learning based classifiers, including linear and non-linear ensemble learning, in addition to one regression based procedure, have been evaluated for the classification of primary headaches within a supervised learning setting, achieving highest aggregate performance outcomes of AUC 0.923, sensitivity 0.897, and overall classification accuracy of 0.843. This study also introduces the proposed HydroApp system, which is an M-health based personalised application for the follow-up of patients with long-term conditions such as chronic headache and hydrocephalus. We managed to develop this system with the supervision of headache specialists at Ashford hospital, London, and neurology experts at Walton Centre and Alder Hey hospital Liverpool. We have successfully investigated the acceptance of using such an M-health based system via an online questionnaire, where 86% of paediatric patients and 60% of adult patients were interested in using HydroApp system to manage their conditions. Features and functions offered by HydroApp system such as recording headache score, recording of general health and well-being as well as alerting the treating team, have been perceived as very or extremely important aspects from patients’ point of view. The study concludes that the advances in intelligent systems and M-health applications represent a promising atmosphere through which to identify alternative solutions, which in turn increases the capacity in the current service model and improves diagnostic capability in the primary headache domain and beyond