Smart clinics have gained much popularity due to the technological advancements in areas like computer vision. The recognition of objects and activities and overall perceiving the environment lies at the core of such systems.
This is essential not just for the eco-independent systems, but also for Human-Machine-Interaction - specially in scenarios with small work-areas like dental treatment. In this paper, we compare a number of machine learning mod-
els (including Multinomial Logistic Regression, Lazy Instance-based Learning (IBk), Sequential Minimal Optimization (SMO), Hoe ding Tree and Random Tree) for robustly identifying dental treatments. We take the objects-focussed
as input, which covers parameters like material, symptoms of the patient teeth and tools used by the dentist. We take advantage of the fact that the issue of identifying a particular treatment can be solved by recognizing the objects
seen during an activity. We collected a dental dataset in-the-wild and ran our tests to find that integrating different parameters improves accuracy relative to using each one separately. However, we also noted that in certain cases using the symptoms stand-alone gave better results. Also, with respect to RMS error convergence, symptoms showed to have lower error compared to combined. Finally, we noticed that the combined approach led to longer build and
test times for the machine learning models. This shows that in machine learning applications in general and in medical/dental applications in particular, adding more parameters does not always lead to improved results. Rather it
depends on the ML tool used, the parameters considered and the data given as input.