Prediction of Corona Virus Disease Outcome and Future Forecast the Trend of COVID-19 in India: Observational Study

This work is motivated by the disease caused by the novel corona virus Covid-19, rapid spread in India. An encyclopaedic search from India and worldwide social networking sites was performed between 1 March 2020 and 20 Jun 2020. Nowadays social network platform plays a vital role to track spreading behaviour of many diseases earlier then government agencies. Here we introduced the approach to predict and future forecast the disease outcome spread through corona virus in society to give earlier warning to save from life threats. We compiled daily data of Covid-19 incidence from all state regions in India. Five states (Maharashtra, Delhi, Gujarat, Rajasthan and Madhya-Pradesh) with higher incidence and other states considered for time series analysis to construct a predictive model based on daily incidence training data. In this study we have applied the predictive model building approaches like k-nearest neighbour technique, Random-Forest technique and stochastic gradient boosting technique in COVID-19 dataset and the simulated outcome compared with the observed outcome to validate model and measure the performance of model by accuracy (ACC) and Kappa measures. Further forecast the future trends in number of cases of corona virus deceased patients using the Holt Winters Method. Time series analysis is effective tool for predict the outcome of corona virus disease. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

A Novel CNN Framework for the Detection of COVID-19 Using Manta Ray Optimization and KNN Classifier in LUS Images

Reverse transcription polymerase chain reaction (RT-PCR) is the gold standard for the diagnosis of COVID-19. Studies have proven that non-invasive techniques based on medical imaging can be used as an alternative to RT-PCR. The use of medical imag-ing technologies along with RT-PCR could improve the diagnosis and management of the disease. Even though several methods exist for diagnosing COVID-19 from X-ray images and CT scans, ultrasound image has not been explored much to diagnose the disease. In this study, we built a deep learning model using ultrasound images for a fast and efficient disease diagnosis. Pre-trained Convolutional Neural Networks (CNN), trained on the ImageNet database has been utilized for feature extraction. The nature-inspired Manta Ray Foraging Optimization (MRFO) algorithm is applied for dimensionality reduction and K-Nearest-Neighbour (KNN) for classification. Model training has been performed using a publicly available POCUS dataset consisting of 2944 ultrasound images sampled from more than 200 Lung Ultrasound (LUS) videos. Experimentations conducted in this study prove the efficiency of the model in the diagnosis of COVID-19. The model achieved an accuracy of 99.4337% using MobilenetV2 as the pre-trained network. © 2023, Ismail Saritas. All rights reserved.

COVID-19 prediction based on GLCM features of radiography image using SVM, KNN, and backpropagation ANN classifiers

COVID-19 is a type of disease that transmits a new variant of virus known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) in the same novel coronavirus family as SARS-CoV and Middle East Respiratory Syndrome Coronovirus (MERS-COV). A fast method to detect the disease is essential to prevent larger transmission and to look after the infected patients. The Chest X-ray, one of the detection methods of COVID-19 can be used in the examination process of suspected cases. In this paper, a COVID-19 detection model through chest x-ray images is proposed by using Grey Level Co-occurrence Matrix (GLCM) with Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Backpropagation Artificial Neural Network (BP-ANN) classifiers. In this case, Principal Component Analysis (PCA) will be added as a mean to optimize features extraction process. The aim of this work is to find the best classifier for predicting chest x-ray images as normal, pneumonia, or COVID-19 suspect. The BP-ANN emerged as the best classifier with 85,5% accuracy, 85,8% precision, and 86,1% recall. © 2023 Author(s).

Predictive model to determine the growth of mobile money transactions in Zambia using data mining techniques

Mobile money has been known to be a successful venture around the world especially so, for African countries due to the many limitations that traditional banks have like operations, expensive transaction costs and cumbersome process to open account to mention but a few. The presence of mobile money has not only allowed the unbanked population to have accounts but has also alleviated poverty for many rural communities. Zambia has seen an increase of mobile money accounts and COVID-19 has exacerbated this increase. Therefore, this paper sought to determine data mining algorithm best predicts mobile money transaction growth. This paper was quantitative in nature and used aggregated monthly mobile money data (from Zambian mobile network operators) from 2013 to 2020 as its sample which was collected from Bank of Zambia and Zambia Information Communications and Technology Authority. The paper further used WEKA data mining tool for data analysis following the Cross-Industrial Standard Process for data mining guidelines. The performance from best to least is K-nearest neighbour, random forest, support vector machines, multilayer perceptron and linear regression. The predictions from data mining techniques can be deployed to predict growth of mobile money and hence be used in financial inclusion policy formulation and other strategies that can further improve service delivery by mobile network operators.

A Novel Approach for Predicting Covid 19 Recovered Cases using Nourishment by Comparing KNN over SVM

To predict the accuracy value of COVID19 recovered number of patients using Nourishment. Material and Methods: For forecasting accuracy percentage of COVID19 recovered patient health diet, Novel K Nearest Neighbour with test size (N=10) and Support Vector Machine with test size (N=10) were iterated 20 times to COVID19 recovered number of patients with g power as 80 %, threshold 0.014 and confidence interval as 95%. Sigmoid function is used in K Nearest Neighbour prediction to probability to help enhance accuracy. Results: In comparison to Support Vector Machine 66% percent Accuracy, Novel K Nearest Neighbour produced substantial results with 94 % Accuracy. Support Vector Machine and K Nearest Neighbour statistical significance is p=1.000(p<0.05) Independent sample T-test value states that the results in the study are significant. Conclusion: KNN is a straightforward and efficient algorithm for quickly building Models of machine learning. KNN predicting COVID19 Health Diet % with more accuracy. © 2022 IEEE.

Detection of COVID-19 Patients using Speech Recognition with Support Vector Machine' and Comparing with 'K Nearest Neighbour Algorithm'

This research endeavor is focused on identifying patients with the Covid-19 virus via the use of a novel voice recognition technique that makes use of a Support Vector Machine (abbreviated as 'SVM') and compares its accuracy with that of 'K-Nearest Neighbor' (abbreviated as 'KNN'). When it comes to speech recognition, the SVM method is regarded to be group 1, and the KNN method is considered to be group 2, and both groups have a total of 20 samples. The outcomes of these data were analyzed using statistical analysis using a'independent sample T-test,' which has a margin of error of 5% and a pretest power of 80%. At a significance of 0.042 (p 0.05), KNN obtains an accuracy of 87.5% whereas SVM achieves an accuracy of 96.5%. As compared to KNN, the prediction accuracy of Covid-19 employing SVM in novel voice recognition achieves much higher levels of accuracy. © 2023 IEEE.

Computer-aided diagnosis of COVID-19 disease from chest X-ray images integrating deep feature extraction

The novel coronavirus (COVID-19) has an enormous impact on the daily lives and health of people residing in more than 200 nations. This article proposes a deep learning-based system for the rapid diagnosis of COVID-19. Chest x-ray radiograph images were used because recent findings revealed that these images contain salient features about COVID-19 disease. Transfer learning was performed using different pre-trained convolutional neural networks models for binary (normal and COVID-19) and triple (normal, COVID-19 and viral pneumonia) class problems. Deep features were extracted from a fully connected layer of the ResNET50v2 model and feature dimension was reduced through feature reduction methods. Feature fusion of feature sets reduced through analysis of variance (ANOVA) and mutual information feature selection (MIFS) was fed to Fine K-nearest neighbour to perform binary classification. Similarly, serial feature fusion of MIFS and chi-square features were utilized to train Medium Gaussian Support Vector Machines to distinguish normal, COVID-19 and viral pneumonia cases. The proposed framework yielded accuracies of 99.5% for binary and 95.5% for triple class experiments. The proposed model shows better performance than the existing methods, and this research has the potential to assist medical professionals to enhance the diagnostic ability to detect coronavirus disease. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Prediction of Unemployment using Machine Learning Approach

Unemployment is a circumstance which arises when people above a specific age are not engaged in any kind of activities which contribute to the economic welfare of the individual and country. Unemployment is becoming a rising concern which is making the daily life of people difficult. Unemployment causes poverty and depression among the citizens. Nowadays there are different opportunities in different sectors. But people are not aware of those opportunities. Different states are there where there is a lack of skilled labour whereas many states are there that have skilled labour but less opportunities. Another reason for unemployment since 2020 is the COVID-19 pandemic. We have selected this topic to spread awareness among the citizens. This work attempts to detect the states of India which are in serious need of increasing employment opportunities. We have applied the concept of Supervised Machine Learning algorithms to detect the states with the lowest employment rate. The data visualization gives a better picture of the trends in unemployment rate over years. There has been a use of different popular algorithms like Logistic Regression, Support Vector Machine, K-nearest neighbors (kNN) Algorithm and Decision Tree. In the end we have tried to find the algorithm which is going to give us more accuracy so that necessary steps can be taken for the employment of the eligible and deserving people. © 2022 IEEE.

Improved Accuracy in Speech Recognition System for Detection of Covid-19 Using K Nearest Neighbour and Comparing with Artificial Neural Network

The objective of this research is to recognize the speech signals for identifying the Covid-19 using K Nearest Neighbour (KNN) and comparing accuracy with an Artificial Neural Network (ANN). Speech recognition using KNN is considered as group 1 and Artificial Neural Network is considered as group 2, where each group has 20 samples. ANN is a machine learning program in which the input is processed by numerous elements and produces the output based on predefined functions. KNN is defined to find the relations between the query and pick the value closest to the query. These groups were analyzed by an independent sample T-test with 5% of alpha, and 80% of pretest power. ANN and KNN achieve an accuracy of 83.5% and 91.49% respectively (significance < 0.05). This analysis observed that KNN has significantly higher accuracy than ANN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification

In many fields, complicated issues can now be solved with the help of Artificial Intelligence (AI) and Machine Learning (ML). One of the more modern Metaheuristic (MH) algorithms used to tackle numerous issues in various fields is the Beluga Whale Optimization (BWO) method. However, BWO has a lack of diversity, which could lead to being trapped in local optimaand premature convergence. This study presents two stages for enhancing the fundamental BWO algorithm. The initial stage of BWO's Opposition-Based Learning (OBL), also known as OBWO, helps to expedite the search process and enhance the learning methodology to choose a better generation of candidate solutions for the fundamental BWO. The second step, referred to as OBWOD, combines the Dynamic Candidate Solution (DCS) and OBWO based on the k-Nearest Neighbor (kNN) classifier to boost variety and improve the consistency of the selected solution by giving potential candidates a chance to solve the given problem with a high fitness value. A comparison study with present optimization algorithms for single-objective bound-constraint optimization problems was conducted to evaluate the performance of the OBWOD algorithm on issues from the 2022 IEEE Congress on Evolutionary Computation (CEC'22) benchmark test suite with a range of dimension sizes. The results of the statistical significance test confirmed that the proposed algorithm is competitive with the optimization algorithms. In addition, the OBWOD algorithm surpassed the performance of seven other algorithms with an overall classification accuracy of 85.17% for classifying 10 medical datasets with different dimension sizes according to the performance evaluation matrix.

Measurement of Blood Oxygen based on Remote-Photoplethysmography

SpO2, also known as blood oxygen saturation, is a vital physiological indicator in clinical care. Since the outbreak of COVID-19, silent hypoxia has been one of the most serious symptoms. This symptom makes the patient's SpO2 drop to an extremely low level without discomfort and causes medical care delay for many patients. Therefore, regularly checking our SpO2 has become a very important matter. Recent work has been looking for convenient and contact-free ways to measure SpO2 with cameras. However, most previous studies were not robust enough and didn't evaluate their algorithms on the data with a wide SpO2 range. In this paper, we proposed a novel non-contact method to measure SpO2 by using the weighted K-nearest neighbors (KNN) algorithm. Five features extracted from the RGB traces, POS, and CHROM signals were used in the KNN model. Two datasets using different ways to lower the SpO2 were constructed for evaluating the performance. The first one was collected through the breath-holding experiment, which induces more motion noise and confuses the actual blood oxygen features. The second dataset was collected at Song Syue Lodge, which locates at an elevation of 3150 meters and has lower oxygen concentration in the atmosphere making the SpO2 drop between the range of 80% to 90% without the need of holding breath. The proposed method outperforms the benchmark algorithms on the leave-one-subject-out and cross-dataset validation. © 2022 IEEE.

Improving Urban Air Quality Monitoring in Delhi, India: Reflections on Low-Cost Air Quality Sensors (LCAQS) and Participatory Engagement

In Delhi, the capital city of India, air pollution has been a perpetual menace to urban sustainability and public health. The present study uses a mixed-method approach to enumerate to the urban authorities: (a) the state of air pollution in the city;(b) systemic flaws in the current monitoring network;(c) potential means to bolster it;and (d) need of a participatory framework for monitoring. Information about Air Quality Index (AQI), obtained from 36 monitoring stations across Delhi is compared between 2021 (20 April–25 May;2nd year/phase of SARS-CoV-2 lockdown), and the corresponding time periods in 2020 (1st year/phase of lockdown), and 2019 (business-as-usual) using the Mann–Whitney U Test. AQI during the 2021 lockdown (a) appeared statistically more similar (p <.01) to that of 2019 and (b) exceeded the environmental health safety benchmark for 85% days during the study period (20 April–25 May). However, this only presented a partial glimpse into the air pollution status. It owes to numerous ‘holes’ in the AQI data record (no data and/or insufficient data). Moreover, certain areas in Delhi yet have no monitoring station, or only too few, to yield a ‘representative’ estimate (inadequate spatial coverage). Such shortcomings in the existing monitoring network may deter future research and targeted/informed decision-making for pollution control. To that end, the present research offers a summary view of Low-Cost Air Quality Sensors (LCAQS), to offer the urban sustainability authorities, ‘complementary’ technique to bolster and diversify the existing network. The main advantages and disadvantages of various LCAQS sensor technologies are highlighted while emphasizing on the challenges around various calibration techniques (linear and non-linear). The final section reflects on the integration of science and technology with social dimensions of air quality monitoring and highlights key requirements for (a) community mobilization and (b) stakeholder engagement to forge a participatory systems’ design for LCAQS deployment. © 2022 National Institute of Urban Affairs.

Addressing Cancer Readmission Prediction Model Drift: A Case Study

The cancer readmission prediction model classifies patients as high-risk or low-risk for readmittance. Consequently, intervention strategies focus on high-risk patients. Nevertheless, the performance of machine learning models generally degrades over time due to changes in the environment that violates models' assumptions, which include statistical data changes and process changes. This research introduces a framework that improves the sensitivity of the cancer readmission prediction model by identifying new features of cancer readmission, such as Diabetes and Anti-Nausea, which potentially cause the model's sensitivity to drift. The proposed model considers these 20 new factors with the 35 original factors that use the most recent dataset to predict cancer readmissions. Recursive feature elimination was used to identify key features. Some of the most popular classification algorithms, which include logistic regression and adaptive boosting, were used to retrain and classify cancer readmissions. The best algorithm was validated on a new dataset that was collected over 11 months, which covered three different waves of Covid-19. The results suggested K-Nearest Neighbors (KNN) algorithm performs the best among all eight studied algorithms. The KNN model incorporated new dominant features that did not exist in the original Random Forest (RF) model. The KNN model has an improvement of 8.05% in sensitivity compared to the RF model. The presence of Covid-19 does not have a significant impact on the performance of the KNN model. The suggested framework identifies potential admitted patients for intervention actions, helps reduce cancer readmission rates, costs, and improves the quality of care for cancer patients. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Analysis and Dynamic Monitoring of Indoor Air Quality Based on Laser-Induced Breakdown Spectroscopy and Machine Learning

The air quality of the living area influences human health to a certain extent. Therefore, it is particularly important to detect the quality of indoor air. However, traditional detection methods mainly depend on chemical analysis, which has long been criticized for its high time cost. In this research, a rapid air detection method for the indoor environment using laser-induced breakdown spectroscopy (LIBS) and machine learning was proposed. Four common scenes were simulated, including burning carbon, burning incense, spraying perfume and hot shower which often led to indoor air quality changes. Two steps of spectral measurements and algorithm analysis were used in the experiment. Moreover, the proposed method was found to be effective in distinguishing different kinds of aerosols and presenting sensitivity to the air compositions. In this paper, the signal was isolated by the forest, so the singular values were filtered out. Meanwhile, the spectra of different scenarios were analyzed via the principal component analysis (PCA), and the air environment was classified by K-Nearest Neighbor (KNN) algorithm with an accuracy of 99.2%. Moreover, based on the establishment of a high-precision quantitative detection model, a back propagation (BP) neural network was introduced to improve the robustness and accuracy of indoor environment. The results show that by taking this method, the dynamic prediction of elements concentration can be realized, and its recognition accuracy is 96.5%.

Exploring Prediction of COVID-19 and its Severity using Machine Learning

COVID-19 has crippled the lives of millions in the world and is continuously doing so without any sight of relief. Even after the roll out of effective vaccines against COVID-19 and more than half of the population inoculated, it is still a widespread concern. This has led to extensive research around the world regarding the prediction of the COVID-19 disease, its diagnosis, developing drugs for its treatment and its forecasting, etc. Machine Learning has proved its significance in almost every domain and its techniques are also being actively used against COVID-19 by the researchers giving satisfactory results. In this paper, we have highlighted some of the efficient research that have been done using machine learning techniques to predict COVID-19 disease and its severity in patients. The performance of those techniques has been discussed and analyzed. We also carried out a comparative analysis of the most common techniques used in terms of accuracy obtained by them. It has been found that Support Vector Machines, Neural Networks and K-Nearest Neighbor models give the best performance in most of the research works. © 2022 IEEE.

Pneumonia identification based on lung texture analysis using modified k-nearest neighbour

Covid-19 is a virus that was first discovered in China, which has the impact of mild and severe respiratory infections such as pneumonia. Pneumonia is inflammation and consolidation of lung tissue due to infectious agents. Generally pneumonia has a high mortality rate, as do Covid-19 patients. For now, it is very difficult to distinguish between Pneumonia and Covid-19, due to the high similarity of X-Ray image results. The high similarity has an impact on the difficulty of difference between Pneumonia and Covid-19 patients. This research aims to be able to different Pneumonia and Covid-19 patients based on texture analysis of the Gray Level Co-Occurrence Matrix using Modified k-Nearest Neighbour as a classifier. The calculations used in the Gray Level Co-Occurrence Matrix method are Contrast, Correlation, Energy, and Homogeneity which will be input for the Modified k-Nearest Neighbour classifier. The results showed that the highest accuracy is when the value of K = 3 using Manhattan Distance and 80%:20% data percentage, which is 87.5%. For the values of K = 7 and K = 9 there is no change in accuracy, so it can be concluded that the value of K that affects accuracy only occurs at the values of K = 3 and K = 5. Then, the higher the K value, the lower the resulting accuracy.

Social Distancing Inspection To Mitigate COVID-19 Using K-Nearest Neighbour

The beauty of Indian culture is to celebrate happiness as well as sorrows in gatherings of relatives, friends, and well-wishers. But with the advent of the COVID-19 pandemic, the world order has changed and social distancing has become inevitable and needs to be observed strictly to save mankind. Keeping in view the transmission of the COVID-19 virus through contacts and non-availability of medicines and vaccines, social distancing is one of the key solutions that is way out to contain this spread of the virus. As the name states, social distancing suggests that people should physically maintain distance between themselves which will reduce the chance of close contact and automatically leads to a decrease in the spread of infectious diseases like the Corona Virus and many other viruses which spread through close contact. Social distancing is possibly the most effective non-pharmaceutical way to prevent the spread of disease. The main objective of social distancing is if people are not close together, they are incapable of spreading germs. This mitigation measure is used to decrease transmission, by suspending and decreasing the size of the epidemic peak and spreading cases over a lengthier period to relieve the burden on the healthcare organization. In this paper, a model is recommended where the total number of people present in the frame is detected using the YOLO object detection algorithm, and distance between each individual is measured Using K-Nearest Neighbour. If the distance between any two individuals is less than 6 feet or 2 meters then a red bounding box pops around them indicating that they are violating the rule of social distancing. This model is implemented on Raspberry Pi with buzzer system for alert. © 2021 IEEE.

Mental Health Analysis of Indians During Pandemic

The Covid-19 pandemic has severely affected many countries around the globe in terms of physically as well as mentally. During the initial months of the pandemic have reported India’s deficient cases, but eventually the cases were proliferated as the time progress. The government’s decision to impose a lockdown without warning has a wide-ranging impact, affecting everyone from low-wage workers to huge corporations. As a result, there is a negative impact on people’s mental health and emotions. The people had suffered from depressions, anxiety, fatigue and so forth. Many wide varieties of the people had expressed their thoughts, viewpoints, and their mental conditions in the form of tweets over the Twitter, a social media platform. Hence, in this paper, we have statistically analysed the data of tweeted tweets to elicit the meaningful insights. The data was analysed using the unsupervised clustering strategy–K-means and LDA–and the results were reinforced and validated using the pre-trained supervised classification approach–Text to Text transformer. The anticipated data depicted that the fear was the most common state of mind at the end of the lockdown, followed by joy, anger, and sadness. Furthermore, the deduced insights will be highly beneficial in decision-making process when such an epidemic or pandemic situation re-surges. © 2021, Springer Nature Switzerland AG.

Computer‐aided diagnosis of COVID‐19 disease from chest x‐ray images integrating deep feature extraction

The novel coronavirus (COVID‐19) has an enormous impact on the daily lives and health of people residing in more than 200 nations. This article proposes a deep learning‐based system for the rapid diagnosis of COVID‐19. Chest x‐ray radiograph images were used because recent findings revealed that these images contain salient features about COVID‐19 disease. Transfer learning was performed using different pre‐trained convolutional neural networks models for binary (normal and COVID‐19) and triple (normal, COVID‐19 and viral pneumonia) class problems. Deep features were extracted from a fully connected layer of the ResNET50v2 model and feature dimension was reduced through feature reduction methods. Feature fusion of feature sets reduced through analysis of variance (ANOVA) and mutual information feature selection (MIFS) was fed to Fine K‐nearest neighbour to perform binary classification. Similarly, serial feature fusion of MIFS and chi‐square features were utilized to train Medium Gaussian Support Vector Machines to distinguish normal, COVID‐19 and viral pneumonia cases. The proposed framework yielded accuracies of 99.5% for binary and 95.5% for triple class experiments. The proposed model shows better performance than the existing methods, and this research has the potential to assist medical professionals to enhance the diagnostic ability to detect coronavirus disease. [ FROM AUTHOR] Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

COVID-19 cough classification using machine learning and global smartphone recordings.

We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone. This type of screening is non-contact, easy to apply, and can reduce the workload in testing centres as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19. The datasets used in this study include subjects from all six continents and contain both forced and natural coughs, indicating that the approach is widely applicable. The publicly available Coswara dataset contains 92 COVID-19 positive and 1079 healthy subjects, while the second smaller dataset was collected mostly in South Africa and contains 18 COVID-19 positive and 26 COVID-19 negative subjects who have undergone a SARS-CoV laboratory test. Both datasets indicate that COVID-19 positive coughs are 15%-20% shorter than non-COVID coughs. Dataset skew was addressed by applying the synthetic minority oversampling technique (SMOTE). A leave-p-out cross-validation scheme was used to train and evaluate seven machine learning classifiers: logistic regression (LR), k-nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM) and a residual-based neural network architecture (Resnet50). Our results show that although all classifiers were able to identify COVID-19 coughs, the best performance was exhibited by the Resnet50 classifier, which was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98. An LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs, with an AUC of 0.94 after selecting the best 13 features from a sequential forward selection (SFS). Since this type of cough audio classification is cost-effective and easy to deploy, it is potentially a useful and viable means of non-contact COVID-19 screening.