A novel machine learning framework for diagnosing the type 2 diabetics using temporal fuzzy ant miner decision tree classifier with temporal weighted genetic algorithm

Abstract

Diabetic is becoming a very serious disease today for the most of people all over the world due to the unhealthy food habits. For predicting the diabetes, we introduce a new diabetic diagnosis system which combines a newly proposed temporal feature selection and temporal fuzzy ant miner tree (TFAMT) classifier for effective decision making in type-2 diabetes analysis. Moreover, a new temporal weighted genetic algorithm is proposed in this work for enhancing the detection accuracy by preprocessing the text and image data. Moreover, intelligent fuzzy rules are extracted from the weighted temporal capabilities with ant miner fuzzy decision tree classifier, and then fuzzy rule extractor is used to reduce the variety of functions in the extracted regulations. We empirically evaluated the effectiveness of the proposed TFAMT–TWGA model using the UCI Repository dataset and the collected retinopathy image dataset. The outcomes are analyzed and as compared with other exiting works. Furthermore, the detection accuracy is proven by way of using the ten-fold cross validation.