Identifying key soil characteristics for Francisella tularensis classification with optimized Machine learning models.

ABSTRACT: Francisella tularensis (Ft) poses a significant threat to both animal and human populations, given its potential as a bioweapon. Current research on the classification of this pathogen and its relationship with soil physical-chemical characteristics often relies on traditional statistical methods. In this study, we leverage advanced machine learning models to enhance the prediction of epidemiological models for soil-based microbes. Our model employs a two-stage feature ranking process to identify crucial soil attributes and hyperparameter optimization for accurate pathogen classification using a unique soil attribute dataset. Optimization involves various classification algorithms, including Support Vector Machines (SVM), Ensemble Models (EM), and Neural Networks (NN), utilizing Bayesian a

SUBMITTER: Ahmad F 

PROVIDER: S-EPMC10799052 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature