Given their good selectivity,high sensitivity and rapid sensing speed,biosensors play an important role in the non-invasive diagnosis of diseases through liquid biopsy.However,with the rapid growth of the quantity and types of data produced by biosensors,the joint analysis of large amounts of biosensing data is faced with sig-nificant challenges.Recently,machine learning has emerged as a prevalent intelligent tool for data mining and processing in biosensor-based disease diagnosis.This review presented an overview of the machine learning algo-rithms and different types of biosensors commonly employed in disease diagnosis systems.It summarized the lat-est progress and achievements in machine learning-assisted biosensors for intelligent diagnosis.Furthermore,we discussed the main challenges and key opportunities in this field.
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