A Revised Adaptive Network-based Fuzzy Inference System Combined with Neural Network to Predict Diabetes

Abstract

Abstract Today, neural network models are widely used to predict whether a person will develop diabetes in the future. However, for fuzzy inference engine and Adaptive Network-based Fuzzy Inference System (ANFIS), it costs a lot when the number of features is large, and the model will be more explainable if we use feature selection methods. In this paper, we modify the ANFIS model, combine ANFIS and neural network, and propose the ANFIS-NN model. We use SMOTE to address the imbalance between different classes, and use RFE and Casual Inference to do feature selection work. Then, we train an ANFIS model, and use a 5-layer neural network to replace the last layers to improve prediction accuracy. Data comparison experiments shows that our models get 0.9812 on accuracy, 0.9790 on G-mean, 0.9776 on F1 score on PIMA dataset using Casual Inference feature selection method, much better than traditional ANFIS; and our model works better on other diabetes datasets. The ANFIS-NN proposed in this paper can also be applied to other datasets to predict diabetes.