Abstract
In this study, the problem of primary headache diagnosis is considered, referring to multiple frames of reference, including the complexity characteristics of living systems, the limitation of human information processing, the enduring nature of headache throughout history, and the potential for intelligent systems paradigms to both broaden and deepen the scope of such diagnostic solutions. In particular, the use of machine learning is recruited for this study, for which a dataset of 836 primary headache cases is evaluated, originating from two medical centres located in Turkey. Five primary headache classes were derived from the data obtained, namely Tension Type Headache (TTH), Chronic Tension Type Headache (CTTH), Migraine with Aura (MwA), Migraine without Aura (MwoA), followed by Trigeminal Autonomic Cephalalgia (TAC). A total of 9 machine learning based classifiers, ranging from linear to non-linear ensembles, in addition to 1 random baseline procedure, were evaluated within a supervised learning setting, yielding highest performance outcomes of AUC 0.985, sensitivity 1, and specificity 0.966. The study concludes that modern computing platforms represent a promising setting through which to realise intelligent solutions, enabling the space of analytical operations needed to drive forward diagnostic capability in the primary headache domain and beyond.
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Magiorkinis, E., Diamantis, A., Mitsikostas, D.D., Androutsos, G.: Headaches in antiquity and during the early scientific era. J. Neurol. 256(8), 1215–1220 (2009)
Diamond, S., Franklin, M.A.: Headache Through the Ages. Professional Communications, New York (2005)
Steiner, T.J., Stovner, L.J., Al Jumah, M., Birbeck, G.L., Gururaj, G., Jensen, R., Katsarava, Z., Queiroz, L.P., Scher, A.I., Tekle-Haimanot, R., et al.: Improving quality in population surveys of headache prevalence, burden and cost key methodological considerations. J. Headache Pain 14(1), 87 (2013)
Clarke, C., MacMillan, L., Sondhi, S., Wells, N.: Economic and social impact of migraine. QJM 89(1), 77–84 (1996)
Lipton, R.B., Stewart, W.F., Simon, D.: Medical consultation for migraine: results from the American migraine study. Headache: J. Head Face Pain 38(2), 87–96 (1998)
La Jolla Donald, J., et al.: Wolf’s Headache and Other Head Pain. Oxford University Press, New York (2001)
Headache Classification Committee of the International Headache Society (HIS): The international classification of headache disorders, (beta version). Cephalalgia, vol. 33(9), pp. 629–808 (2013)
Martin, V.T.: The diagnostic evaluation of secondary headache disorders. Headache J. Head Face Pain 51(2), 346–352 (2011)
Ahn, J.Y., Han, K.S., Oh, S.Y., Lee, C.D.: An application of interval-valued intuitionistic fuzzy sets for medical diagnosis of headache. Int. J. Innov. Comput. Inf. Control 7(5), 2755–2762 (2011)
Ahn, J.Y., Park, J.H.: Headache diagnosis method using an aggregate operator. CSAM (Communications for Statistical Applications and Methods) 19(3), pp. 359–365 (2012)
Mendes, K.B., Fiuza, R.M., Teresinha, M., Steiner, A.: Diagnosis of headache using artificial neural networks. J. Comput. Sci. 10(7), 172–178 (2010)
Andrew, M.E., Penzien, D.B., Rains, J.C., Knowlton, G.E., McAnulty, R.D.: Development of a computer application for headache diagnosis: The headache diagnostic system. Int. J. Bio-Med. Comput. 31(1), 17–24 (1992)
Celik, U., Yurtay, N., Koc, E.R., Tepe, N., Gulluoglu, H., Ertas, M.: Diagnostic accuracy comparison of artificial immune algorithms for primary headaches. Comput. Math. Methods Med. 2015, 8 (2015)
Celik, U., Yurtay, N., Yurtay, Y.: Headache diagnosis with k-means algorithm. Glob. J. Technol. 1, 1074–1081 (2012)
Tezel, G., Kose, U.: Headache disease diagnosis by using the clonal selection algorithm. In: 6th International Advanced Technologies Symposium, pp. 144–148 (2011)
Hussain, A.J., Fergus, P., Al-Askar, H., Al-Jumeily, D., Jager, F.: Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women. Neurocomputing 151, 963–974 (2015)
Al-Jumeily, D., Hussain, A., Fergus, P.: Using adaptive neural networks to provide self-healing autonomic software. Int. J. Space-Based Situated Comput. 5(3), 129–140 (2015)
Ghazali, R., Hussain, A.J., Al-Jumeily, D., Lisboa, P.: Time series prediction using dynamic ridge polynomial neural networks. In: 2009 Second International Conference on Developments in eSystems Engineering (DESE), pp. 354–363. IEEE (2009)
Klingberg, T.: Limitations in information processing in the human brain: neuroimaging of dual task performance and working memory tasks. Prog. Brain Res. 126, 95–102 (2000)
Simon, H.A.: Models of Man: Social and Rational. Wiley, New York (1957)
Crombie, D., Cross, K., Fleming, D.: The problem of diagnostic variability in general practice. J. Epidemiol. Community Health 46(4), 447–454 (1992)
Dietterich, T.G.: Ensemble learning. In: The Handbook of Brain Theory and Neural Networks, vol. 2, pp. 110–125 (2002)
Balakrishnama, S., Ganapathiraju, A.: Linear discriminant analysis-a brief tutorial. Institute for Signal and information Processing, vol. 18 (1998)
Zweig, M.H., Campbell, G.: Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39(4), 561–577 (1993)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Lipton, R.B., Diamond, S., Reed, M., Diamond, M.L., Stewart, W.F.: Migraine diagnosis and treatment: results from the American migraine study II. Headache J. Head Face Pain 41(7), 638–645 (2001)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
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Keight, R., Aljaaf, A.J., Al-Jumeily, D., Hussain, A.J., Özge, A., Mallucci, C. (2017). An Intelligent Systems Approach to Primary Headache Diagnosis. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_6
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