Automatic Diagnosis Metabolic Syndrome via a K−Nearest Neighbour Classifier

Omar Behadada, Meryem Abi-Ayad, Georgios Kontonatsios, Marcello Trovati

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

3 Citations (Scopus)
157 Downloads (Pure)


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 cardiovasculardiseasesandtype2diabetes.Specifically,weemploy the K−Nearest neighbour (KNN) 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 KNN classifier. For evaluation, we apply the proposed kNN algorithm against two baseline machine learning classifiers, namely Nave Bayes and an artificial Neural Network, on a manually curated dataset of 64 individuals. The results that we obtained 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.
Original languageEnglish
Title of host publicationNot Known
Publication statusE-pub ahead of print - 13 Apr 2017
Event12th International GPC Conference, GPC 2017 - Cetara, Italy
Duration: 11 May 201714 May 2017


Conference12th International GPC Conference, GPC 2017


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