Author : Satpute Mayuri B. and Dr. Vinod V. Kimbahune

Recently there has been a significant increase in the number of medical help websites that provide medical assistance to the people visiting for a variety of symptoms  and illnesses. This platform’s popularity can be attributed to the increased convenience offered to the patients who can get diagnosed from the comfort of their homes. The medical professionals on the website provide professional counseling and also take appointments for various specializations. As the interaction takes place on an online portal mostly in the form of text, to the medical professional or a chat bot, the deficiency in the patient’s medical terminology can be detrimental as there is a significant risk that the doctor missed a part of the symptom’s explanation. This is a highly problematic occurrence and the solution to this problem is the formulation of an efficient medical text classification technique. Therefore, this paper outlines an innovative technique that utilizes the Bayesian Classification model along with the addition of Artificial Neural Networks (ANN) coupled with Natural Language Processing (NLP) which achieves highly accurate Medical Text  Classification.

Keywords:  Natural Language Processing, TF-IDF,  Artificial Neural Network, Bayesian classification