Hirsutism: A multivariate approach of feature selection and classification

Abstract

Abstract Armanino, C., Lanteri, S., Forina, M., Balsamo, A., Migliardi, M. and Cenderelli, G., 1989. Hirsutism: a multivariate approach of feature selection and classification. Chemometrics and Intelligent Laboratory Systems, 5: 335–341. Supervised pattern recognition methods were applied to the results of seven hormonal tests from a population of twenty-six healthy subjects and one hundred and seven women affected by hirsutism, in order to study the discriminant information from analytical data. Eigenvector projection and raw Varimax rotation, a stepwise multivariate method of feature selection based on quadratic discriminant analysis, the classification methods of k-nearest neighbours and quadratic discriminant analysis were applied. The prediction ability of the multivariate normal models, built by five selected variables (testosterone—estradiol binding globulin, dehydroepiandrosterone sulphate, estrone, salivary testosterone, 17β-estradiol) was 87.5%. Hierarchical clustering was carried out on the analytical data from the group of hirsute patients: two principal clusters and one singleton were identified.