The proposed method indicates an inexpensive, portable, and easily accessible method for the quantitative analysis of medical samples for the detection of disease in the enzyme-linked immunosorbent assay (ELISA). The procedure follows a point-of-care diagnostic model and attends to the several challenges in healthcare system in rural settings. The proposed technique will alleviate the inconveniences faced by the average citizen of a country with insufficient resources to implement an affordable healthcare administration for its entire population. A smartphone is used to procure images of an ELISA containing para-nitrophenol samples which is then fed into a machine learning algorithm, specifically artificial neural network. The introduction of two relatively new technologies in medical aid-the smartphone and machine learning not only reduces cost and time of detection, but also presents ample possibility for further development. The predictions result in highly accurate diagnostic labels. The same method can be used for blood samples for the prediction of presence of any disease, provided adequate training set has been deployed.