book

Enabling Healthcare 4.0 for Pandemics

by Abhinav Juneja, Vikram Bali, Sapna Juneja, Vishal Jain, Prashant Tyagi

September 2021

Intermediate to advanced

352 pages

8h 42m

English

Wiley-Scrivener

Read now

Unlock full access

1.1 Introduction1.2 COVID-19 Diagnosis in Patients Using Machine Learning1.3 AI and Machine Learning as a Support System for Robotic System and Drones1.4 ConclusionReferences
2.1 Introduction2.2 Key Techniques of HCS 4.0 for COVID-192.3 Real World Applications of HCS 4.0 for COVID-192.4 Opportunities and Limitations2.5 Future Perspectives2.6 ConclusionReferences
3.1 Introduction3.2 Literature Review3.3 Types of Machine Learning3.4 Machine Learning Algorithms3.5 Analysis and Prediction of COVID-19 Data3.6 Analysis Using Machine Learning Models3.7 Conclusion & Future ScopeReferences
4.1 Introduction4.2 Effect of COVID-19 on Different Sections of Society4.3 Definition and Types of Machine Learning4.4 Machine Learning Approaches for COVID-19References
5.1 Introduction5.2 Related Work5.3 Suggested Methodology5.4 Models in Epidemiology5.5 Particle Filtering Algorithm5.6 MCM Model Implementation5.7 Diagnosis of COVID-195.8 ConclusionReferences

6.1 Introduction6.2 Technological Strategies to Support Society During the Pandemic6.3 Feasible Prospective Technologies in Controlling the Pandemic6.4 Coronavirus Pandemic: Emerging Technologies That Tackle Key Challenges6.5 The Golden Age of Drone Delivery6.6 Technology Helps Pandemic Management6.7 ConclusionReferences
7.1 Introduction7.2 Review of Technologies Used During the Outbreak of Ebola and SARS7.3 Emerging Technological Solutions to Mitigate the COVID-19 Crisis7.4 ConclusionReferences
8.1 Introduction8.2 Issues and Challenges Due to Pandemic8.3 Digital Technology and Pandemic8.4 Application of Technology for Handling Pandemic8.5 Challenges with Digital Healthcare8.6 ConclusionReferences
9.1 Introduction9.2 Related Work9.3 Technologies to Combat COVID-199.4 Comparison of Various Technologies to Combat COVID-199.5 ConclusionReferences
10.1 Introduction to Pandemic10.2 Technique Used to Handle Pandemic Challenges10.3 Working Process of Techniques10.4 Data Analysis10.5 Rapid Development Structure10.6 Conclusion & Future ScopeReferences
11.1 Introduction11.2 Methodology11.3 Computational Results11.4 ConclusionReferences
12.1 Introduction12.2 Background and Objectives12.3 The Functioning of Multi-Purpose Robot12.4 Discussion and ConclusionsReferences
13.1 Introduction13.2 What is IoT?13.3 Various Models Proposed for Managing a Pandemic Like COVID-19 Using IoT13.4 Global Technological Developments to Overcome Cases of COVID-1913.5 Results & Discussions13.6 ConclusionReferences
14.1 Introduction14.2 Epidemiology and Etiology14.3 Transmission of Infection and Available Treatments14.4 COVID-19 Infection and Immune Responses14.5 Mathematical Modeling14.6 ConclusionReferences
15.1 Introduction15.2 Our Contribution15.3 State-Wise Data Set and Analysis15.4 Neural Network15.5 Results and Discussion15.6 Conclusion15.7 Future ScopeReferences

Content preview from Enabling Healthcare 4.0 for Pandemics

15Machine Learning: A Tool to Combat COVID-19

Shakti Arora¹ ^*, Vijay Anant Athavale¹ ^† and Tanvi Singh² ^‡

¹Department of Computer Science and Engineering, Panipat Institute of Engineering & Technology, Panipat, India

²Department of Civil Engineering, Panipat Institute of Engineering & Technology, Panipat, India

Abstract

COVID-19 has become a global challenge and is threatening mankind. The global economy is in crisis due to a long tranche of partial to complete lockdown. Forecasting the number of COVID-19 cases is a challenge as cases are both symptomatic as well as asymptomatic, recurrence after recovery is another challenge. Careful data analysis is required to predict and estimate the number of affected cases as well as death ratio. During this pandemic situation, forecasting uncertainty is of utmost importance in decision making. In this chapter, authors have developed a model to predict the COVID-19 confirmed cases. The prediction is based on the data collected in different phases of lockdown in India. In this study, a model is developed using machine learning approaches based on the analysis of data of two Indian states Delhi and Maharashtra where maximum infected cases are found. This study is an attempt to help the decision-makers in better planning and actions. In this study, Neural Network (NN) and M5P model trees are applied to forecast the number of infected cases with each progressive day. Results suggest that the performance of the neural network-based model is ...