Analysis of COVID-19 Complications Using Deep Learning-Based Neuro-Fuzzy Classification Approach

Abstract

Introduction: Nowadays, the use of technology in medical diagnosis, management, and patient care has exploded. Medical diagnosis is a difficult task that is frequently performed by professional developers. This inductive research objective is to investigate advanced machine learning techniques for effectively analyzing health data based on COVID-19 symptoms. There are numerous variables to consider when evaluating the disease, and determining the effect of COVID-19 on various human organs is not an easy task. Objective: This research aims to develop an adaptive medical diagnosis model for COVID-19 to ascertain and predict disease risk and detection. Methods: Frequently used models for classification are Adaptive Neuro-Fuzzy Inference System (ANFIS) and Deep learningbased Neural Networks (DNN). This article employs a Deep Neuro-Fuzzy System with a cooperative structure in its analysis. Results: This article predicts disease using a patient dataset from Mexico with over twenty input parameters or features. To develop a more accurate classification technique, the results of several Deep learning and Neuro-Fuzzy mechanisms are compared and analyzed. This study’s outcome can be extended to a larger number of input features and applied to the detection of additional diseases. Conclusion: The proposed Deep Learning-Based Neuro-Fuzzy classification model shows better complications and prediction results compared to others.