MICROBE’S PATHOGENICITY PREDICTION ON COMPLEX OF INDERECTIVE NATIVE FEATURES
Article is devoted to quality evaluation of a microbe’s pathogenicity detection on complex of native features directly not related with human health hazard (cell shape, Gram stain, pH and temperature optimums, habitat, motility), obtained in vivo for developing methods of occupational morbidity analysis and prediction. Samples clasterization was conducted using unsupervised Random Forest algorithm. Model quality was estimated with SVM-algorithm on parameters of Accuracy and Sensitivity (model’s ability to classify pathogenic samples correctly) from confusion matrix. To reveal significant predicts five models were built consequentially excluding predicts with lowest Gini impurity. Most accurate model (#3, Accuracy - 78,21%, Sensitivity - 69,23%) were built on four predicts - cell shape, habitat, pH and temperature optimums. Most sensitive (wherein less accurate) and consequently most effective model (#4, Accuracy - 66,67%, Sensitivity - 87,18%) in a microbe’s pathogenicity detection. This model built on three predicts - habitat, pH and temperature optimums.