Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control

Increasing the growth of big data, particularly in healthcare-Internet of Things (IoT) and biomedical classes, tends to help patients by identifying the disease early through methods for the analysis of medical data. Hence, nanotechnology-based IOT biosensors play a significant role in the medical f...

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Autores principales:	Vikas Maheshwari, Md Rashid Mahmood, Sumukham Sravanthi, N. Arivazhagan, A. ParimalaGandhi, K. Srihari, R. Sagayaraj, E. Udayakumar, Yuvaraj Natarajan, Prashant Bachanna, Venkatesa Prabhu Sundramurthy
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/2b41494f7f554397a5b5265b951320b4
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spelling	oai:doaj.org-article:2b41494f7f554397a5b5265b951320b42021-11-15T01:19:34ZNanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control1687-412910.1155/2021/3383146https://doaj.org/article/2b41494f7f554397a5b5265b951320b42021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3383146https://doaj.org/toc/1687-4129Increasing the growth of big data, particularly in healthcare-Internet of Things (IoT) and biomedical classes, tends to help patients by identifying the disease early through methods for the analysis of medical data. Hence, nanotechnology-based IOT biosensors play a significant role in the medical field. Problem. However, the consistency continues to decrease where missing data occurs in such medical data from nanotechnology-based IOT biosensors. Furthermore, each region has its own special features, which further lowers the accuracy of prediction. The proposed model initially reconstructs lost or partial data in order to address the challenge of handling the medical data structures with incomplete data. Methods. An adaptive architecture is proposed to enhance the computing capabilities to predict the disease automatically. The medical databases are managed by unpredictable environments. This optimized paradigm for diagnosis produces the fuzzy, genetically categorized decision tree algorithm. This work uses a normalized classifier namely fuzzy-based decision tree (FDT) algorithm for classifying the data collected via nanotechnology-based IOT biosensors, and this helps in the identification of nondeterministic instances from unstructured datasets relating to the medical diagnosis. The FDT algorithm is further enhanced by using genetic algorithms for effective classification of instances. Finally, the proposed system uses two larger datasets to verify the predictive precision. In order to describe a fuzzy decision tree algorithm based upon the fitness function value, a modified decision classification rule is used. The structure and unstructured databases are configured for processing. Results and Conclusions. This evaluation of test patterns helps to track the efficiency of FDT with optimized rules during the training and testing stages. The proposed method is validated against nanotechnology-based IOT biosensors data in terms of accuracy, sensitivity, specificity, and F-measure. The results of the simulation show that the proposed method achieves a higher rate of accuracy than the other methods. Other metrics relating to the model with and without feature selection show an improved sensitivity, specificity, and F-measure rate than the existing methods.Vikas MaheshwariMd Rashid MahmoodSumukham SravanthiN. ArivazhaganA. ParimalaGandhiK. SrihariR. SagayarajE. UdayakumarYuvaraj NatarajanPrashant BachannaVenkatesa Prabhu SundramurthyHindawi LimitedarticleTechnology (General)T1-995ENJournal of Nanomaterials, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995
spellingShingle	Technology (General) T1-995 Vikas Maheshwari Md Rashid Mahmood Sumukham Sravanthi N. Arivazhagan A. ParimalaGandhi K. Srihari R. Sagayaraj E. Udayakumar Yuvaraj Natarajan Prashant Bachanna Venkatesa Prabhu Sundramurthy Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control
description	Increasing the growth of big data, particularly in healthcare-Internet of Things (IoT) and biomedical classes, tends to help patients by identifying the disease early through methods for the analysis of medical data. Hence, nanotechnology-based IOT biosensors play a significant role in the medical field. Problem. However, the consistency continues to decrease where missing data occurs in such medical data from nanotechnology-based IOT biosensors. Furthermore, each region has its own special features, which further lowers the accuracy of prediction. The proposed model initially reconstructs lost or partial data in order to address the challenge of handling the medical data structures with incomplete data. Methods. An adaptive architecture is proposed to enhance the computing capabilities to predict the disease automatically. The medical databases are managed by unpredictable environments. This optimized paradigm for diagnosis produces the fuzzy, genetically categorized decision tree algorithm. This work uses a normalized classifier namely fuzzy-based decision tree (FDT) algorithm for classifying the data collected via nanotechnology-based IOT biosensors, and this helps in the identification of nondeterministic instances from unstructured datasets relating to the medical diagnosis. The FDT algorithm is further enhanced by using genetic algorithms for effective classification of instances. Finally, the proposed system uses two larger datasets to verify the predictive precision. In order to describe a fuzzy decision tree algorithm based upon the fitness function value, a modified decision classification rule is used. The structure and unstructured databases are configured for processing. Results and Conclusions. This evaluation of test patterns helps to track the efficiency of FDT with optimized rules during the training and testing stages. The proposed method is validated against nanotechnology-based IOT biosensors data in terms of accuracy, sensitivity, specificity, and F-measure. The results of the simulation show that the proposed method achieves a higher rate of accuracy than the other methods. Other metrics relating to the model with and without feature selection show an improved sensitivity, specificity, and F-measure rate than the existing methods.
format	article
author	Vikas Maheshwari Md Rashid Mahmood Sumukham Sravanthi N. Arivazhagan A. ParimalaGandhi K. Srihari R. Sagayaraj E. Udayakumar Yuvaraj Natarajan Prashant Bachanna Venkatesa Prabhu Sundramurthy
author_facet	Vikas Maheshwari Md Rashid Mahmood Sumukham Sravanthi N. Arivazhagan A. ParimalaGandhi K. Srihari R. Sagayaraj E. Udayakumar Yuvaraj Natarajan Prashant Bachanna Venkatesa Prabhu Sundramurthy
author_sort	Vikas Maheshwari
title	Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control
title_short	Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control
title_full	Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control
title_fullStr	Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control
title_full_unstemmed	Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control
title_sort	nanotechnology-based sensitive biosensors for covid-19 prediction using fuzzy logic control
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/2b41494f7f554397a5b5265b951320b4
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Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control

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