Role of Machine Learning Approaches in Predicting COVID-19 New Active Cases Using Multiple Models

The coronavirus epidemic began in Wuhan and has already spread to practically every country on the planet. Conravirus has a big population in India, and people are becoming infected at an alarming rate. Machine learning algorithms have been utilized to find trends in the number of active cases owing to COIVD in India and the state of Odisha in this study. The data was gathered from the WHO and studied to see if there was a link between the number of current cases, those who died, and those who recovered. The model was entirely based on multiple regression, support vector machine, and random forest which fits as an effective tool for prediction and error reduction. Based on the dataset taken from March 16, 2020, to August 20, 2020, from the ICMR website, the mean absolute error (MAE) of SVM is less for Odisha and multiple linear regression is less for India. The multiple learner regression model is able to predict number of active cases properly as its R2 score value are 1 and 0.999 for Odisha and India, respectively. Machine leaning model helps us to find trends of effected cases accurately. The model is able to predict what extent the COVID cases will grow or fall in the next 30 days which enables us to be prepared in advance and take some preventive measures to fight against this deadly COVID virus. It is observed that features are positively correlated with each other. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Study and impact analysis of COVID-19 pandemic clinical data on infection spreading

In December 2019, a severe pneumonialike disease has occurred in the city of Wuhan, Hubei Province in China. Within a very short period the infection spread across the whole world, but there was no previous medical history about this virus and how, where, and when the disease infected the human body and mutated in humans is still unknown. Subsequently, the coronavirus disease 2019 (COVID-19) outbreak was declared as the world pandemic on March 2020 by the World Health Organization because of its harmfulness and super spreading nature. Till now, there is no specific medications and clinical treatment available to avoid this pandemic COVID-19 outbreak. For this, it is essential to have a detailed study and analysis through the recent technologies. The recent trends such as artificial intelligence and machine learning (ML) based models can learn from past patient medication data and can suggest improvement accordingly by analyzing the data to control the spread. In the present scenario, the correct decision could be the appropriate precaution to stop spreading as well as controlling such a pandemic disease by proposing predictive ML that analyzes past data and conclude some useful information for future control of the spread of COVID-19 infections using minimum resources. The ML-based approach can be helpful to design different models to give a predictive solution for controlling infection and spreading and taking precaution toward the COVID-19 outbreak. In this chapter, we study the basic information of COVID-19 and its effectiveness worldwide. We also state the fundamental steps of ML, discuss the ML mechanism to study the pandemic for research and academic purposes, and study the data analytics of clinical data of India through a case study. As the data is a time series data, we analyze the data from March 1, 2020 to April 15, 2020;the decision tree approach of ML is discussed through a case study. Finally, the chapter is concluded with certain future scope of work in this area of research. © 2022 Elsevier Inc.

SensMask: An intelligent mask for assisting patients during COVID-19 emergencies

A new pandemic disease named as novel corona virus disease (COVID-19) was discovered during end of 2019 in Wuhan city of China and was quickly spread throughout the globe. But, till now no medicine is available to fight against the infection caused by the disease. The infection may also be transmitted easily from person to person through highly infectious nasal droplets when a healthy person comes in contact with a distance of less than 1m from the affected person. The doctors, physicians as well as nurses consult the patients very closely to assess the health conditions of the affected persons and there is a great chance that they may carry infection from them. In this work, we have proposed an intelligent mask to assist COVID-19 patients/doctors/nurses with an innovative SensMask to address this issue. This mask contains GPS sensor, IR proximity sensor, walk sensor and FS5 sensor. All patients need to wear this mask to observe the health details. Such sensors monitor health data from patients and send it to the cloud through the home/hospital-based local cloudlet. The cloudlet information is used by physicians for further diagnosis of patients. This proposed approach was simulated and the results obtained indicate that it helps in maximizing throughput and reduced delay. © 2021 Instituto Politecnico Nacional. All rights reserved.

COVTrac: Covid-19 Tracker and Social Distancing App

In order to break the chain of SARS-CoV-2 virus infection, most countries are taking the help of mobile apps. The existing apps lack the necessary features to address the major issues of detection and alert of social distancing, detection and prevention of large gatherings, easy generation of travel permit during lock down, etc. The existing apps do not provide any solution for alerting user of the users social distance violation. Though many of these apps are being used for contact tracing, they do not say which places the user has visited recently which is a crucial parameter in contact tracing and preventing community spread. Smartphones empowered with the latest technology like Google geo-location and low energy Bluetooth (BLE) with such an app can complement a country’s general Covid-19 control strategies—comprising testing, contact tracing, seclusion and social distancing. In this work, we develop a Covid-19 tracker and social distancing app named as COVTrac, an Android app that uses various capabilities of the smartphone to address these issues with the existing mobile app along with a number of other concerns such as privacy, accurate contact tracing and prevention of socio-physical interactions. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 risk assessment using the C4.5 algorithm

The number of confirmed cases of COVID-19 is increasing exponentially day by day across the world because of its super spreading nature. It was started in China and took a very less time to spread all over the globe. Due to its mortality rate, spreading nature, and unavailability of proper medicine and vaccination, it is declared as a pandemic by the World Health Organization (WHO) in March 2020. In this crisis time of the COVID-19 outbreak, technologists try to smooth the lives by minimizing the infection rate and facilitating in-time quality treatment. In this work, we collected the world data of COVID-19 cases in terms of confirmed, recovery, active, and death and provided visualization. We have also tried to find the patient’s risk level in terms of high, medium, and low by analyzing the patient’s symptoms and previous health histories such as high blood pressure, cardiac disease, diabetes, kidney issues, and others. We applied the C4.5 machine learning (ML) classifier to the considered dataset after preprocessing for risk assessment. The results obtained from the study indicate that the algorithm helps in achieving 75% accuracy. © Springer Nature Switzerland AG 2021.

Application of deep learning strategies to assess COVID-19 patients

The recently identified viral disease caused by the coronavirus is named as COVID-19. Many COVID-19-affected patients develop mild to severe respiratory failure and heal without specific intervention. Older persons and individuals with serious medical problems such as cardiovascular disease, asthma, chronic respiratory disorders and cancer continue to experience extreme disease. Throughout the battle against coronavirus, there were several studies about the use of AI. A global overview of the deep learning strategies, which have been used until now, and the potential course of study, is quite relevant in the present scenarios. Thus, it is very much essential to study and analyse the AI techniques available in the literature to be utilized to assess COVID-19 patients. In this work, we have used deep learning strategies on both CT scan and X-ray images to assess COVID-19 patients. © Springer Nature Switzerland AG 2021.