Computer-implemented machine learning system for mycobacterium bovis detection using microrna biomarker classification

Abstract

A computer-implemented ensemble machine learning system for detecting Mycobacterium bovis infection processes qPCR-derived miRNA expression data through trained classification algorithms. The system receives quantitative data for a miRNA panel, calculates delta-Cq values, in some embodiments normalized said delta-Cq values, and applies classification models. The machine learning engine generates classification probability scores processed through threshold comparison logic to produce binary diagnostic classifications with 91% accuracy, 0.89 sensitivity, and 0.92 specificity. In some embodiment, an ensemble approach computationally combines multiple algorithmic outputs to improve diagnostic reliability over single-classifier systems, enabling automated bovine tuberculosis detection that overcomes technical limitations of conventional testing methods for rapid, field-deployable herd management decisions.

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