Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES TO COVID-19 OUTBREAK : A SURVEY Sowmya H.K1, Jesy Janet Kumari J2, Dr. R. Ch. A. Naidu3, K.Vengatesan4 1,2,3 Department of CSE, The Oxford College of Engineering Bommanahalli, Bangalore-560068 Professor, Computer Engineering, Sanjivani College of Engineering, Kopargaon [email protected],[email protected],[email protected], [email protected] ABSTRACT Corona viruses are group of viruses which may cause illness in both animals and humans. A person can easily get COVID-19 from the other people who have the virus. It is a serious disease, which disseminates through tiny water droplets from the nose or mouth, which are thrown out when a COVID-19 infected person coughs, sneezes, or speaks. This disease was first discovered in China and has since spread throughout the world at breakneck speed. At this pandemic time, the entire world should take an adequate and efficient step to analyze the disease and get rid of the effects of this epidemic. The applications of Machine Learning (ML) and Artificial Intelligence (AI) techniques play an important role to detect and predict potential effect of this virus in future by gathering and examining most recent and past data. Furthermore, it can be used in realizing and recommending the enhancement of a vaccine for COVID-19. This paper focuses on reviewing the role of AI and ML approaches used for examining, analyzing, predicting, contact tracking of existing patients and potential patients. Keywords: Corona virus, Machine Learning, Artificial Intelligence, contact tracking, epidemic I. INTRODUCTION Coronavirus disease (COVID-19) is a newly identified virus that causes an infectious disease. The form of the infection is due to the Severe Acute Respiratory Syndrome Corona Virus -2 (SARS-CoV-2) from the coronovirus family. COVID-19 will be spread primarily through droplets produced by infected people coughing, sneezing, or exhaling. SARS- CoV-2 infections are accepted to spread between people through introduction to respiratory beads ousted during hacking or wheezing from irresistible people. This infection can stay airborne for longer periods however may cause rapidly, contingent upon ecological conditions. Individuals will be tainted by breathing the infection if closeness of somebody who has COVID-19 or by contacting a polluted surface likes eyes, nose or mouth. This pandemic emerged in terrain China, in the city of Wuhan, Hubei. The episode keeps on spreading everywhere on the world, so World Health Organization pronounced the pandemic as a pestilence. The tale Corona infection (SARS- CoV-2) sickness began spreading to in excess of 185 nations. Individuals can ensure themselves by washing hands as often as possible or cleaning with liquor based cleanser, trying not to contact the eyes, mouth, and nose with messy hands, when coughing or sniffling, use a tissue to cover your mouth and nose, remaining at any rate 6 feet from others. Artificial Intelligence (AI) and Machine Learning (ML) are assuming an essential function in an imaginative innovation which is useful to battle against the COVID-19 pandemic. AI is an innovation method to gather information, to anticipate danger of disease and to foresee who is at high danger. It allows computers to emulate human intelligence and consume vast amounts of data in order to find patterns and insights quickly. The Covid-19 outbreak will be the subject of this review article, as well as how AI and machine learning technologies are being used to solve the problems that occur throughout the blaze. www.turkjphysiotherrehabil.org 690 Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X II. APPLICATIONS OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE TO THE COVID-19 EPIDEMIC In the COVID-19 pandemic response, machine learning-based approaches are playing an important part. AI is being used by experts to think about the infection, test potential drugs, evaluate people, and look at the overall health consequences, among other things. Computer-based intelligence and machine learning (ML) are being used to increase forecast accuracy for both irresistible and non- irresistible diseases. Recent research demonstrates how artificial intelligence and machine learning techniques aid health care professionals in combating the spread of communicable and non-communicable diseases, particularly in low-income countries. III. COVID-19 SCREENING AND MEDICATION USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE TECHNIQUES The identification of pandemic infection at prior stage is significant errand to give earlier medicine to protect human existence. The powerful analytical process aids in the cost-effective prevention of pandemic diseases thereby speeding up the examination process. In comparison to the traditional approach, developing an expert system for medical care assistance for the detection, screening, and management of Covid -19 infection is more cost effective. Different calculations of Machine Learning and Artificial Intelligence are utilized to upgrade the assessment and screening measure with the assistance of radiology pictures, for example, patient's Computed Tomography (CT) pictures, X-Ray pictures, and Clinical blood test information. Table 1 depicts the procedure, sort, and size of the data used to perform the detection and screening of Corona infection illness from this perspective. To improve conventional strategy for determination and screening, medical care professionals utilize X-Ray and CT images of the distinguished patient as standard tool. Shockingly, the presentation of such techniques is low and not relevant during the flare-up of Covid-19 pandemic. In such manner, late exploration study portrays that, the scientists could separate 11 key blood lists from 253 clinical blood tests and can be utilized as an instrument to help medical services specialists toward quick analysis of Covid- 19 sickness [1]. The Random Forest algorithm was used by the authors of this paper to extract the features, which they discovered with an accuracy level of 95.95 percent and a specificity of 96.97 percent. The authors further presented the Covid – 19 assistant discrimination tools, which takes 11 parameters and calculates and displays whether a sample is COVID-19 patient with a high chance of being diagnosed. Despite the fact that the proposed tool must be clinically tested several times, it provides some novel insight into the rapid diagnosis of COIVD-19 infection. Recent examination shows the capability of AI and ML devices by recommending another model that accompanies fast and legitimate strategy Covid-19 conclusion utilizing Deep Convolutional Network. The examination [2] shows the exhibition of a specialist framework practicing Artificial Intelligence and Machine Learning on 1136 CT Images of 723 Covid-19 tainted patients, recommends the utilization of the Deep Learning Segmentation Classification Model as an assistant device for radiologist coming about 97.4%, 92.2% of affectability and particularity separately. Ongoing examinations [3] shows that creation of extra instrument for the analysis of Covid-19 with Convolutional Neural Network called DarkCovidNet Architecture dependent on Deep Learning calculation raise the exactness. The developed model was based on 127 contaminated patients' primitive x Ray images, and it accurately predicted the exhibition, with a precision of 98.08 percent for parallel class and 87.02 percent for multi-class. Moreover, specialists have utilized Support Vector Machine (SVM) as an element order model [4] on clinical highlights, for example, mixes of clinical, lab highlights, and segment data utilizing GHS, CD3 rate, all out protein, and patient. In anticipating critical Covid cases in patients, this latest design is both effective and accurate. The model's robustness is demonstrated by an AUROC of 0.9996 for training data and 0.9757 for testing data. Researchers used deep learning algorithms to build a large data repository of X-rays and CT scan images from different sources, and suggested an efficient COVID-19 detection technique [5]. On the prepared dataset of X-rays and CT scan images, a simple convolution neural network (CNN) and a modified pre-trained AlexNet model are used. Empirical findings show that using a pre-trained network and a modified CNN, the proposed models could produce 98 percent and 94.1 percent accuracy, respectively. www.turkjphysiotherrehabil.org 691 Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X TABLE 1. ML AND AI APPROACH FOR COVID - 19 TESTING Author AI and ML method Kind of data Sample size Accuracy Wu, J.et. al. [1] Random Forest Clinical, Demographic Total of 253 samples 95.95% Algorithm from 169 Covid-19 suspicious patients Shuo Jin [2] Deep Learning Model Clinical 1136 CT 97% Images, 723 positives for COVID-19 Ozturk, T. et.al. CNN and DarkCovi Clinical, 127 X- 98.08% [3] dNet Mammographic ray images Sun, L et. al.[4] SVM Clinical, laboratory 336 infected 77.5% Characteristics patients, 26 Demographic severe/critical cases Halgurd S [5] CNN and Modified Clinical 170 X- 98% AlexNet model ray images, 361 CT images Xiaowei Xu et. al Deep Learning ResNet Clinical 618 CT 86.7 % [6] location- attention samples Model Ongoing Research [6] uncovers that, authors proposed an early screening model to draw a differentiation of Covid- 19 illness from Influenza-A