Prediction Of Periodontal Disease Using Modified Anfis Based Classifier Model

Abstract

The ultimate objective of this research is to predict periodontal disease and model a computer-based decision-making system based on a modified adaptive neuro-fuzzy inference system (M-ANFIS). The accuracy has computed for predicting and diagnosing periodontal disease. This work concentrates on the optimality of (M-ANFIS) algorithm. The performance of predictive accuracy, specificity, sensitivity, (Receiver Operating Characteristics) ROC, and a confusion matrix to be determined with (M-ANFIS) algorithm. The dataset validation with the collected dataset from available online sources partitioned for testing and training purposes has been performed.  Evaluating and optimizing the dataset has to be performed with the M-ANFIS classifier model. The proposed decision-making process has achieved a productive and efficient method of predicting and diagnosing periodontal disease.