Abstract
In this paper, we investigate the automatic diagnosis of patients with metabolic syndrome, i.e., a common metabolic disorder and a risk factor for the development of cardiovascular diseases and type 2 diabetes. Specifically, we employ the k-Nearest neighbour (k-NN) classifier, a supervised machine learning algorithm to learn to discriminate between patients with metabolic syndrome and healthy individuals. To aid accurate identification of the metabolic syndrome we extract different physiological parameters (age, BMI, level of glucose in the blood etc.) that are subsequently used as features in the k-NN classifier. For evaluation, we compare the proposed k-NN algorithm against two baseline machine learning classifiers, namely Naïve Bayes and an artificial Neural Network. Cross-validation experiments on a manually curated dataset of 64 individuals demonstrate that the k-NN classifier improves upon the performance of the baseline methods and it can thus facilitate robust and automatic diagnosis of patients with metabolic syndrome. Finally, we perform feature analysis to determine potential significant correlations between different physiological parameters and the prevalence of the metabolic syndrome.
This is a preview of subscription content, log in via an institution to check access.
References
Li, J., Bioucas-Dias, J.M., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Trans. Geosci. Remote Sens. 48(11), 4085–4098 (2010)
Bohning, D.: Multinomial logistic regression algorithm. Ann. Inst. Stat. Math. 44(1), 197–200 (1992)
Camps-Valls, G., Bruzzone, A.: Kernel-based methods for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 43(6), 1351–1362 (2005)
Behadada O., Trovati M., Chikh M.A. and Bessis N.: Big data-based extraction of fuzzy partition rules for heart arrhythmia detection: a semi-automated approach. Concurrency Comput.: Pract. Exp. (2015)
Biino, G., Concas, M.P., Cena, H., Parracciani, D., Vaccargiu, S., Cosso, M., Marras, F., D’ Esposito, V., Beguinot, F., Pirastu, M.: Dissecting metabolic syndrome components: data from an epidemiologic survey in a genetic isolate. SpringerPlus 4(1), 324 (2015)
Jaspinder, K.: A comprehensive review on metabolic syndrome. Cardiol. Res. Pract. 2014, 943162 (2014). doi:10.1155/2014/943162
Meigs, J.B.: Invited commentary: insulin resistance syndrome? Syndrome X? Multiple metabolic syndrome? A syndrome at all? Factor analysis reveals patterns in the fabric of correlated metabolic risk factors. Am. J. Epidemiol. 152, 908–912 (2000). doi:10.1093/aje/152.10.908
Alberti, K.G., Zimmet, P.Z.: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet. Med. 15, 539–553 (1998). doi:10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S
Alberti, K.G., Eckel, R.H., Grundy, S.M., Zimmet, P.Z., Cleeman, J.I., Donato, K., et al.: Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; american heart association; world heart federation; international. Circulation 120, 1640–1645 (2009). doi:10.1161/CIRCULATIONAHA.109.192644
Belur, V.D.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. Mc Graw-Hill Computer Science Series. IEEE Computer Society Press, Las Alamitos (1991)
Lihua, Y., Qi, D., Yanjun, G.: Study on KNN text categorization algorithm. Micro Comput. Inf. 21, 269–271 (2006)
Suguna, N., Thanushkodi, K.: An improved k-nearest neighbor classification using genetic algorithm. Int. J. Comput. Sci. Issues 7(2), 18–21 (2010)
Eckel, R.H., Grundy, S.M., Zimmet, P.Z.: The metabolic syndrome. Lancet 365(9468), 1415–1428 (2005)
Heier, E.C., Meier, A., Julich-Haertel, H., Djudjaj, S., Rau, M., Tschernig, T., Geier, A., Boor, P., Lammert, F. and Lukacs-Kornek, V.: Murine CD103+ dendritic cells protect against steatosis progression towards steatohepatitis. Journal of Hepatology (2017)
Blachier, M., Leleu, H., Peck-Radosavljevic, M., Valla, D.C., Roudot-Thoraval, F.: The burden of liver disease in Europe: a review of available epidemiological data. J. Hepatol. 58(3), 593–608 (2013)
Worachartcheewan, A., Nantasenamat, C., Isarankura-Na-Ayudhya, C., Pidetcha, P., Prachayasittikul, V.: Identification of metabolic syndrome using decision tree analysis. Diab. Res. Clin. Pract. 90(1), e15–e18 (2010)
Makrilakis, K., Liatis, S., Grammatikou, S., Perrea, D., Stathi, C., Tsiligros, P., Katsilambros, N.: Validation of the finnish diabetes risk score (FINDRISC) questionnaire for screening for undiagnosed typpe 2 diabetes, dysglycaemia and the metabolic syndrome in Greece. Diab. Metab. 37(2), 144–151 (2011). Vancouver
Helminen, E.E., Mntyselk, P., Nyknen, I., Kumpusalo, E.: Far from easy and accurate-detection of metabolic syndrome by general practitioners. BMC Fam. Pract. 10(1), 76 (2009)
Ushida, Y., Kato, R., Niwa, K., Tanimura, D., Izawa, H., Yasui, K., Takase, T., Yoshida, Y., Kawase, M., Yoshida, T., Murohara, T.: Combinational risk factors of metabolic syndrome identified by fuzzy neural network analysis of health-check data. BMC Med. Inf. Decis. Making 12(1), 80 (2012)
De Kroon, M.L., Renders, C.M., Kuipers, E.C., van Wouwe, J.P., Van Buuren, S., De Jonge, G.A., Hirasing, R.A.: Identifying metabolic syndrome without blood tests in young adults? The Terneuzen Birth Cohort. Eur. J. Public Health 18(6), 656–660 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Behadada, O., Abi-Ayad, M., Kontonatsios, G., Trovati, M. (2017). Automatic Diagnosis Metabolic Syndrome via a \(k-\)Nearest Neighbour Classifier. In: Au, M., Castiglione, A., Choo, KK., Palmieri, F., Li, KC. (eds) Green, Pervasive, and Cloud Computing. GPC 2017. Lecture Notes in Computer Science(), vol 10232. Springer, Cham. https://doi.org/10.1007/978-3-319-57186-7_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-57186-7_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57185-0
Online ISBN: 978-3-319-57186-7
eBook Packages: Computer ScienceComputer Science (R0)