Optimal Feature Selection and Classification of Respiratory Diseases by Novel EFICNN-EBOA Algorithm: A Real Time Implementation Concept

Authors

  • R. Rampriya Research Scholar, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Annamalainagar 608002, INDIA
  • N. Suguna Associate Professor, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Annamalainagar 608002, INDIA
  • R. G. Suresh Kumar Professor & Head, Department of Computer Science and Engineering, Rajiv Gandhi College of Engineering & Technology, Pondicherry, INDIA
  • P. Sudhakar Faculty of Engineering and Technology, Annamalai University, Annamalainagar 608002, INDIA

Keywords:

Convolutional Neural Network, Enhanced Feature Interpreted, Election-Based Optimization Algorithm, ESP8266 microcontroller, Internet of Things, Optimal Feature Selection, Real Time Implementation, Respiratory Diseases Classification

Abstract

Currently, many people pass away every day as a result of various respiratory illnesses. The ability to precisely identify various kinds of disorders has been made possible through respiratory sound evaluation. Till now, lung disorders were detected manually by listening for breathing sounds, but this method is no longer practical for a number of factors, including audio quality and medical preferences. With the use of recent computer evaluation, illnesses may be more accurately diagnosed and treated early for patients. Hence, it is crucial to use Artificial Intelligence (AI) approaches to totally simplify the detection of respiratory disorders. This research work aims to perform optimal feature selection and classification of respiratory diseases using novel deep learning methodology. In the initial step, the respiratory data are collected in real time from the abnormal and normal persons with the help of a breathing sensor. Next, the gathered data undergo the data augmentation process for eradicating the overfitting problems. The output attained from this data augmentation process is subjected to the feature extraction phase for extracting the features. From these extracted features, the optimal features are selected. These optimal features undergo the final classification phase done by the novel Enhanced Feature Interpreted Convolutional Neural Network (EFICNN). The parameter tuning in both the optimal feature selection and the classification phases is performed by a nature inspired meta heuristic optimization algorithm referred as Election-Based Optimization Algorithm (EBOA) in order to derive the accuracy maximization as the fitness function. The final output is done on the basis of different ranges of the Peak Expiratory Flow Rate (PEFR) values. This research indicates an efficient and effective method for early detection and remote monitoring of respiratory problems, ultimately resulting in enhanced patient care and quick response to situations of emergency. It does this by integrating the ESP8266 microcontroller with the robust Internet of Things (IoT) platform from Ubidots and using a Peak Flow Meter to input data.

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Published

27.10.2023

How to Cite

Rampriya, R. ., Suguna, N. ., Kumar, R. G. S. ., & Sudhakar, P. . (2023). Optimal Feature Selection and Classification of Respiratory Diseases by Novel EFICNN-EBOA Algorithm: A Real Time Implementation Concept. International Journal of Intelligent Systems and Applications in Engineering, 12(2s), 699–712. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3746

Issue

Vol. 12 No. 2s (2024)

Section

Research Article

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