Study and Analysis of an Approach Towards the Classification of Tooth Wear in Dentistry Using Machine Learning Technique

In current decades, an automatic detection system in healthcare field is an emergent research area. Extensive use of machine learning and deep learning algorithms can be observed in fields of medicine. A lot of research work is done in medicine but some area of oral health care in still unexplored. The emphasis is given to this field only because it is a difficult mission, due to the shortage of precise data available in dentistry for diagnosis and prediction. In this paper, an approach towards the diagnosis, prediction, and classification of oral health diseases in dentistry is implemented and discussed with the help of machine learning techniques on the self-created dataset. Dental tooth wear is considered for evaluation with its different sub categories. Mainly tooth wear is multifactorial situation, leading to the loss of dental hard tissues. Physiological enamel wears is slow process that usually does not lead to any subjective symptoms. When situation progresses periodically, it becomes difficult to restore it to normal phase easily. So to prevent this loss experimentation is performed to diagnose, classify and predict disease at an early stage. This experimentation is done by using several machine learning algorithms and its performance evaluation is done.