Advances in Fuzzy-Based Internet of Medical Things (IoMT)

13Feature Extraction and Diagnosis of Heart Diseases Using Fuzzy-Based IoMT

Tribhangin Dichpally*, Yatish Wutla, Vallabhaneni Uday and Rohith Sai Midigudla

Department of Computer Science Engineering with Specialization in Artificial Intelligence and Machine Learning, Vellore Institute of Technology, Amaravati, India

Abstract

Asthma, cancer, heart disease, and diabetes are prevalent global health challenges. Distinguishing between various types of heart diseases can be challenging. The utilization of smart wearables necessitates the implementation of fog computing and Internet of Things (IoT) solutions in medical diagnosis. The utilization of edge, fog, and cloud computing is recommended for achieving efficient and precise results. The hardware is responsible for the collection of patient data. Cardiac properties are assessed by extracting signals. The compilation of feature extraction from other properties is also undertaken. The optimized cascaded convolution neural network (CCNN) is utilized by the diagnostic system to obtain these properties. The hyperparameters of the CCNN are optimized through the utilization of galactic swarm optimization (GSO). The GSO-CCNN model exhibits an accuracy of 3.70% when compared to other models such as LSTM, CNN, and CCNN. The results indicate that the proposed approach can be considered a dependable improvement over the current approach.

Keywords: IoMT, database, CNN, galactic of utilization, GSO

13.1 Introduction

The current economy relies ...