Identification of Most Relevant Features for Classification of Francisella tularensis using Machine Learning | Bentham Science

摘要

Background: Francisella tularensis is a stealth pathogen fatal for animals and humans.Ease of its propagation, coupled with high capacity for ailment and death makes it a potentialcandidate for biological weapon.Objective: Work related to the pathogen’s classification and factors affecting its prolongedexistence in soil is limited to statistical measures. Machine learning other than conventionalanalysis methods may be applied to better predict epidemiological modeling for this soil-bornepathogen.Methods: Feature-ranking algorithms namely; relief, correlation and oneR are used for soilattribute ranking. Moreover, classification algorithms; SVM, random forest, naive bayes, logisticregression and MLP are used for classification of the soil attribute dataset for Francisellatularensis positive and negative soils.Results: Feature-ranking methods concluded that clay, nitrogen, organic matter, soluble salts, zinc,silt and nickel are the most significant attributes while potassium, phosphorous, iron, calcium,copper, chromium and sand are the least contributing risk factors for the persistence of thepathogen. However, clay is the most significant and potassium is the least contributing attribute.Data analysis suggests that feature-ranking using relief produced classification accuracy of 84.35%for multilayer perceptron; 82.99% for linear regression; 80.27% for SVM and random forest; and78.23% for naive bayes, which is better than other ranking methods. MLP outperforms otherclassifiers by generating an accuracy of 84.35%, 82.99% and 81.63% for feature-ranking usingrelief, correlation and oneR algorithms, respectively.Conclusion: These models can significantly improve accuracy and can minimize the risk ofincorrect classification. They further help in controlling epidemics and thereby minimizing thesocio-economic impact on the society.