Non-invasive prediction mechanism for COVID-19 disease using machine learning algorithms

被引：0

作者：

Bhardwaj, Arnav ^{[1
]}

Agarwal, Hitesh ^{[1
]}

Rani, Anuj ^{[2
]}

Srivastava, Prakash ^{[3
]}

Kumar, Manoj ^{[4
]}

Gupta, Sunil ^{[5
]}

机构：

[1] Amity Univ, Dept Comp Sci & Engn, ASET, Noida, Uttar Pradesh, India

[2] GL Bajaj Inst Technol & Management, Dept Comp Sci, Greater Noida, India

[3] Graph Era Deemed Be Univ, Dept Comp Sci & Engn, Dehra Dun, India

[4] Univ Wollongong Dubai, Fac Engn & Informat Sci, Dubai Knowledge Pk, Dubai, U Arab Emirates

[5] Univ Petr & Energy Studies, Sch Comp Sci, Dehra Dun, Uttarakhand, India

来源：

INTERNATIONAL JOURNAL OF CRITICAL INFRASTRUCTURES | 2024年 / 20卷 / 02期

关键词：

COVID-19; non-invasive; symptoms; machine learning;

D O I：

10.1504/IJCIS.2024.137406

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper has focused on developing a model to detect non-diagnostically whether the person is infected with the COVID-19 disease using all relevant symptoms and details mentioned by the person and then comparing it with a pre-defined dataset of positive cases using machine learning. Different models have been developed to predict the same but none of them focused on the detection of COVID-19 based on symptoms. In a developing nation with a huge population, where the diagnostic availability is scarce, just scanning the body temperature will not help in detection of COVID-19 of a particular individual. This paper presents a model that can predict COVID-19 cases without any testing kit to an accuracy of 99.30%, performing better than other similar approaches with objective to put forward a method that can reduce the need of producing testing kits and also the need to wait for hours before we get the results.

引用

页码：111 / 124

页数：15

共 14 条

[1] COVID-19 medical papers have fewer women first authors than expected [J].

Andersen, Jens Peter ;

Nielsen, Mathias Wullum ;

Simone, Nicole L. ;

Lewiss, Resa E. ;

Jagsi, Reshma .

ELIFE, 2020, 9 :1-7

[2] Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020 [J].

Gupta, Sonal ;

Raghuwanshi, Gourav Singh ;

Chanda, Arnab .

SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 728

[3] Improvements to Platt's SMO algorithm for SVM classifier design [J].

Keerthi, SS ;

Shevade, SK ;

Bhattacharyya, C ;

Murthy, KRK .

NEURAL COMPUTATION, 2001, 13 (03) :637-649

[4] Prediction of COVID-19-Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine [J].

Khan, Muhammad Attique ;

Kadry, Seifedine ;

Zhang, Yu-Dong ;

Akram, Tallha ;

Sharif, Muhammad ;

Rehman, Amjad ;

Saba, Tanzila .

COMPUTERS & ELECTRICAL ENGINEERING, 2021, 90

[5] Sparse multinomial logistic regression: Fast algorithms and generalization bounds [J].

Krishnapuram, B ;

Carin, L ;

Figueiredo, MAT ;

Hartemink, AJ .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2005, 27 (06) :957-968

[6] Machine learning for assisting cervical cancer diagnosis: An ensemble approach [J].

Lu, Jiayi ;

Song, Enmin ;

Ghoneim, Ahmed ;

Alrashoud, Mubarak .

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2020, 106 :199-205

[7] A model based study on the dynamics of COVID-19: Prediction and control [J].

Mandal, Manotosh ;

Jana, Soovoojeet ;

Nandi, Swapan Kumar ;

Khatua, Anupam ;

Adak, Sayani ;

Kar, T. K. .

CHAOS SOLITONS & FRACTALS, 2020, 136

[8] Can a Paper-Based Device Trace COVID-19 Sources with Wastewater-Based Epidemiology? [J].

Mao, Kang ;

Zhang, Hua ;

Yang, Zhugen .

ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2020, 54 (07) :3733-3735

[9] Automated detection of COVID-19 cases using deep neural networks with X-ray images [J].

Ozturk, Tulin ;

Talo, Muhammed ;

Yildirim, Eylul Azra ;

Baloglu, Ulas Baran ;

Yildirim, Ozal ;

Acharya, U. Rajendra .

COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 121

[10] COVID-19 pandemic: Practical considerations on the organization of an allergy clinic-An EAACI/ARIA Position Paper [J].

Pfaar, Oliver ;

Klimek, Ludger ;

Jutel, Marek ;

Akdis, Cezmi A. ;

Bousquet, Jean ;

Breiteneder, Heimo ;

Chinthrajah, Sharon ;

Diamant, Zuzana ;

Eiwegger, Thomas ;

Fokkens, Wytske J. ;

Fritsch, Hans-Walter ;

Nadeau, Kari C. ;

O'Hehir, Robyn E. ;

O'Mahony, Liam ;

Rief, Winfried ;

Sampath, Vanitha ;

Schedlowski, Manfred ;

Torres, Maria Jose ;

Traidl-Hoffmann, Claudia ;

Wang, De Yun ;

Zhang, Luo ;

Bonini, Matteo ;

Brehler, Randolf ;

Brough, Helen Annaruth ;

Chivato, Tomas ;

Del Giacco, Stefano R. ;

Dramburg, Stephanie ;

Gawlik, Radoslaw ;

Gelincik, Asli ;

Hoffmann-Sommergruber, Karin ;

Hox, Valerie ;

Knol, Edward F. ;

Lauerma, Antti ;

Matricardi, Paolo M. ;

Mortz, Charlotte G. ;

Ollert, Markus ;

Palomares, Oscar ;

Riggioni, Carmen ;

Schwarze, Juergen ;

Skypala, Isabel ;

Untersmayr, Eva ;

Walusiak-Skorupa, Jolanta ;

Ansotegui, Ignacio J. ;

Bachert, Claus ;

Bedbrook, Anna ;

Bosnic-Anticevich, Sinthia ;

Brussino, Luisa ;

Canonica, Giorgio Walter ;

Cardona, Victoria ;

Carreiro-Martins, Pedro .

ALLERGY, 2021, 76 (03) :648-676

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