A Machine Learning Approach for Oral Cancer Detection Using Enhanced Multi-Layer Perceptron

Abstract

Oral Cancer is one of the main issues today, diagnosing cancer in prior stage is still difficult for doctors. Applying the data mining techniques of oral cancer diagnosis to get compelling outcomes and accomplishing the solid execution is helpful in medicinal service industry. Generally machine learning is used to empower a program to analyze data. Data mining and machine learning algorithm assume an essential part in therapeutic zone. Data mining is used to find designs in extensive informational collection, including techniques at the intersection of machine learning, statistics, and database frameworks. So treatment is fruitful just if the sore is analyzed early. In this paper, to construct a data mining model for Early Detection and Prediction (ED&P) of oral cancer utilizing machine learning system. The proposed component creating the following stages are data gathering, data preprocessing, clustering and classification. Enhanced Multi-Layer Perceptron (EMLP) algorithm is used for classification or characterization. It is the most significant algorithm which is equipped for performing classification. Enhanced Multi-Layer Perceptron algorithm is proposed in this paper to detect the correct pathological condition related to oral cancer. Database of 1000 patients has been made with 25 properties.