Towards Edge Artificial Intelligence for Predicting Antimicrobial Resistance in Mycobacterium tuberculosis

The main research contribution of this paper is the creation of a lightweight machine learning method. This can run on edge devices to predict resistance in Mycobacterium tuberculosis using whole‑genome sequences. Existing methods usually need features that use a lot of computing power or need prior knowledge of resistance variants. The new model instead uses a 256‑dimensional 4‑mer frequency vector fed into a Random Forest classifier. This change cuts the feature space by orders of magnitude while retaining strong predictive ability. The result is a tool that works well in hospitals in low‑ and middle‑income countries where computers are slow and internet is weak. An average ROC‑AUC of 0.820 is achieved across 20 antibiotics.