Machine learning based Predictive Modeling of Plasma Treatment in Biomedical Surfaces

Plasma medicine is considered as a future research field which mainly consists of science, physics and clinical medicine. For the therapeutic outcome research purpose, based on thermal, electrical and chemical effects, CAP is used. Plasma treatment in terms of biomedical surfaces is difficult as it creates more complexity, thus CAP is proposed. Systematic way of approving plasma treatment creates more inadequate prediction. CAP does give less treatment in plasma surface modeling based on. CAP is used by the human who attains less accuracy as the error rate of accuracy gets high due to handheld devices by the human. CAP is used to identify different parameter which is monitored and controlled for Plasma-based treatments and its various effects. CAP generally supports inbuilt UV radiation gas which determines biomedical surfaces of the plasma. In this novel, the machine learning is used to determine the data set with supervised learning based upon the classification and prediction of the data which are driven and analyzed for the Plasma surface using CAP. In this paper, it proposes the effective way of using CAP in plasma surface treatment in an automated way over the human body.