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 potential candidate for biological weapon.
Objective:
Work related to the pathogen’s classification and factors affecting its prolonged existence in soil is limited to statistical measures. Machine learning other than conventional analysis methods may be applied to better predict epidemiological modeling for this soil-borne pathogen.
Method:
Feature-ranking algorithms namely; relief, correlation and oneR are used for soil attribute ranking. Moreover, classification algorithms; SVM, random forest, naive bayes, logistic regression and MLP are used for classification of the soil attribute dataset for Francisella tularensis positive and negative soils.
Results:
Feature-ranking methods conclude; 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 least contributing risk factors for the persistence of the pathogen. 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; and 78.23% for naive bayes, which is better than other ranking methods. MLP outperforms other classifiers by generating an accuracy of 84.35%,82.99% and 81.63% for feature-ranking using relief, correlation and oneR algorithms, respectively.
Conclusion:
These models can significantly improve accuracy and can minimize the risk of incorrect classification. They further help in controlling epidemics and thereby minimizing the socio-economic impact on the society.
Investigating the role of Simpson’s paradox in the analysis of top-ranked features in high-dimensional bioinformatics datasets
