  Biological cells, by definition, are the basic units which contain the
fundamental molecules of life of which all living things are composed.
Understanding how they function and differentiating cells from one another
therefore is of paramount importance for disease diagnostics as well as
therapeutics. Sensors focusing on the detection and stratification of cells
have gained popularity as technological advancements have allowed for the
miniaturization of various components inching us closer to Point-of-Care (POC)
solutions with each passing day. Furthermore, Machine Learning has allowed for
enhancement in analytical capabilities of these various biosensing modalities,
especially the challenging task of classification of cells into various
categories using a data-driven approach rather than physics-driven. In this
review, we provide an account of how Machine Learning has been applied
explicitly to sensors that detect and classify cells. We also provide a
comparison of how different sensing modalities and algorithms affect the
classifier accuracy and the dataset size required.
