Adaptive Probabilistic Neuro-Fuzzy System and its Hybrid Learning in Medical Diagnostics Task

Yevgeniy Bodyanskiy,Olha Chala,Anastasiia Deineko,Iryna Pliss

doi:10.2174/18750362021140100123

Abstract

Background: The medical diagnostic task in conditions of the limited dataset and overlapping classes is considered. Such limitations happen quite often in real-world tasks. The lack of long training datasets during solving real tasks in the problem of medical diagnostics causes not being able to use the mathematical apparatus of deep learning. Additionally, considering other factors, such as in a dataset, classes can be overlapped in the feature space; also data can be specified in various scales: in the numerical interval, numerical ratios, ordinal (rank), nominal and binary, which does not allow the use of known neural networks. In order to overcome arising restrictions and problems, a hybrid neuro-fuzzy system based on a probabilistic neural network and adaptive neuro-fuzzy interference system that allows solving the task in these situations is proposed. Methods: Computational intelligence, artificial neural networks, neuro-fuzzy systems compared to conventional artificial neural networks, the proposed system requires significantly less training time, and in comparison with neuro-fuzzy systems, it contains significantly fewer membership functions in the fuzzification layer. The hybrid learning algorithm for the system under consideration based on self-learning according to the principle “Winner takes all” and lazy learning according to the principle “Neurons at data points” has been introduced. Results: The proposed system solves the problem of classification in conditions of overlapping classes with the calculation of the membership levels of the formed diagnosis to various possible classes. Conclusion: The proposed system is quite simple in its numerical implementation, characterized by a high speed of information processing, both in the learning process and in the decision-making process; it easily adapts to situations when the number of diagnostics features changes during the system's functioning.

Highlights

Data mining methods are currently widely used in the analysis of medical information [1 - 3] and, first of all, in diagnosis problems based on the available data on the patient's state
The proposed probabilistic neuro-fuzzy system (Fig. 1) contains six layers of information processing: the first hidden layer of fuzzification, formed by one-dimensional bell-shaped membership functions, the second hidden layer - aggregation one, formed by elementary multiplication blocks, the third hidden layer of adders, the number of which is determined by the number of classes plus one per which should be split the original data array, the fourth - defuzzification layer, formed by division blocks, at the outputs of which signals appear that determine the probabilities of belonging Pj(x) of each observation to each of the possible classes, the fifth layer is formed with nonlinear activation functions, and the sixth – formed with division blocks number of which is defined by the number of diagnoses and one summation
In order to solve the problem of classification with data that can be represented in different scales, the adaptive probabilistic neuro-fuzzy system is developed, based on a classical probabilistic neural network, which is able to work under the condition of short datasets

Summary

Introduction

Data mining methods are currently widely used in the analysis of medical information [1 - 3] and, first of all, in diagnosis problems based on the available data on the patient's state. Probabilistic Neural Networks (PNNs) [15, 16] are well suited for solving recognition-classification problems under conditions of a limited amount of training data [17], which, are crisp systems operating in conditions of nonoverlapping classes and learning in a batch mode. The medical diagnostic task in conditions of the limited dataset and overlapping classes is considered. The lack of long training datasets during solving real tasks in the problem of medical diagnostics causes not being able to use the mathematical apparatus of deep learning. Considering other factors, such as in a dataset, classes can be overlapped in the feature space; data can be specified in various scales: in the numerical interval, numerical ratios, ordinal (rank), nominal and binary, which does not allow the use of known neural networks. In order to overcome arising restrictions and problems, a hybrid neuro-fuzzy system based on a probabilistic neural network and adaptive neuro-fuzzy interference system that allows solving the task in these situations is proposed

Methods

Results

Discussion

Conclusion