We propose a low-cost, compact, and field-portable 3D printed holographic microscope for automated cell identification based on a common path shearing interferometer setup. Once a hologram is captured from the portable setup, a 3D reconstructed height profile of the cell is created. We extract several morphological cell features from the reconstructed 3D height profiles, including mean physical cell thickness, coefficient of variation, optical volume (OV) of the cell, projected area of the cell (PA), ratio of PA to OV, cell thickness kurtosis, cell thickness skewness, and the dry mass of the cell for identification using the random forest (RF) classifier. The 3D printed prototype can serve as a low-cost alternative for the developing world, where access to laboratory facilities for disease diagnosis are limited. Additionally, a cell phone sensor is used to capture the digital holograms. This enables the user to send the acquired holograms over the internet to a computational device located remotely for cellular identification and classification (analysis). The 3D printed system presented in this paper can be used as a low-cost, stable, and field-portable digital holographic microscope as well as an automated cell identification system. To the best of our knowledge, this is the first research paper presenting automatic cell identification using a low-cost 3D printed digital holographic microscopy setup based on common path shearing interferometry.