Evolutionary and Ruzzo–Tompa optimized regulatory feedback neural network based evaluating tooth decay and acid erosion from 5 years old children

Abstract

Now-a-days most of the children faced tooth decay and acid erosion problem in their teeth because of continuous bacterial infection, acid segregation, presents of food particles in teeth and so on. Especially, children are more affected by tooth decay, that leads to create severe problem like gingivitis, teeth loss and teeth pain. Due to the importance of tooth decay it needs to predict in earlier condition for eliminating children teeth problem such anorexia and bulimia disorders. Hence the bacterial infection of teeth is critical to be predicted from affected teeth. So, in this paper we analyze the tooth decay and acid erosion from European teeth biomedical data portal which collects information from children having age 5. The teeth decay activities are monitored by evolutionary multi-objective cuckoo feature selection (EMOCA) algorithm with Ruzzo–Tompa optimized regulatory feedback neural network (RTRFNN) that successfully analyze the changes and characteristics of children teeth biomedical teeth data. The introduced method effectively evaluates children tooth data before making the final decision about tooth decay and acid erosion. Then the excellence of the system is evaluated with the help of the experimental results, Ruzzo–Tompa optimized regulatory feedback neural network recognize the abnormal dental features with 99.22% of accuracy.