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.
|Title of host publication||Not Known|
|Publication status||E-pub ahead of print - 13 Apr 2017|
|Event||12th International GPC Conference, GPC 2017 - Cetara, Italy|
Duration: 11 May 2017 → 14 May 2017
|Conference||12th International GPC Conference, GPC 2017|
|Period||11/05/17 → 14/05/17|
Behadada, O., Abi-Ayad, M., Kontonatsios, G., & Trovati, M. (2017). Automatic Diagnosis Metabolic Syndrome via a K−Nearest Neighbour Classiﬁer. In Not Known (pp. 627-637) https://doi.org/10.1007/978-3-319-57186-7_45