Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model

Gupta, Vishan Kumar; Gupta, Avdhesh; Kumar, Dinesh; Sardana, Anjali

doi:10.26599/bdma.2020.9020016

Cited by 128 publications

(71 citation statements)

References 5 publications

(7 reference statements)

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“…Upon close observation of the characteristics of the other models, it became clear that a simpler model such as logistic regression (F1 score = 0.51) cannot capture the complexity of the dataset, even after selecting the best features and tuning the hyperparameters. In various other studies with similar models ( Prakash, 2020 ; Gupta V. K. et al., 2021 ), it was observed that logistic regression underperformed. In our study, random forest, multilayer perceptron, XGBoost, and SVM performed much better and had similar scores, so we can use the results of the four combined to arrive at a prediction.…”

Section: Discussionmentioning

confidence: 89%

“…To avoid information leaking into the test set and overfitting of data, nested CV effectively uses a series of different train–test set splits in each iteration. Nested CV is the preferred way to evaluate and compare tuned ML models and has been used before in clinical settings ( Gupta V. K. et al., 2021 ). Figure 2 demonstrates the workflow of the nested CV algorithm.…”

Section: Methodsmentioning

confidence: 99%

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Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification

Bhat

Pandey

Kanakan

et al. 2021

Front. Cell. Infect. Microbiol.

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The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model.

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Section: Discussionmentioning

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification

Bhat

Pandey

Kanakan

et al. 2021

Front. Cell. Infect. Microbiol.

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show abstract

“…The main limitation of the study is that we analyze, due to lacking data, only 16 European countries. However, unlike previous studies are limited in size examining just a single country 36 – 38 , 40 , our goal is to use the data from 16 European countries to analyze with ML techniques the global effect of different health system characteristics on COVID-19 lethality during the early days of the pandemic.…”

Section: Discussionmentioning

confidence: 99%

The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic

Giancotti

Lopreite

Mauro

et al. 2021

Sci Rep

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This article examines the main factors affecting COVID-19 lethality across 16 European Countries with a focus on the role of health system characteristics during the first phase of the diffusion of the virus. Specifically, we investigate the leading causes of lethality at 10, 20, 30, 40 days in the first hit of the pandemic. Using a random forest regression (ML), with lethality as outcome variable, we show that the percentage of people older than 65 years (with two or more chronic diseases) is the main predictor variable of lethality by COVID-19, followed by the number of hospital intensive care unit beds, investments in healthcare spending compared to GDP, number of nurses and doctors. Moreover, the variable of general practitioners has little but significant predicting quality. These findings contribute to provide evidence for the prediction of lethality caused by COVID-19 in Europe and open the discussion on health policy and management of health care and ICU beds during a severe epidemic.

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“…Besides, ELM tends to present fewer optimization constraints, greater scalability, superior results for generalization performance, and lower learning speed concerning the others models. Gupta et al [2021] analyzed different cases of COVID-19 in India (confirmed, killed, and recovery), using machine learning models: Random Forest, Linear Model, SVM, Decision Tree e Neural Network. The results revealed that the Random Forest presented a superior performance compared to the other evaluated models.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Temporal Bias in Time Series Event Detection Methods

Escobar

Salles

Lima

et al. 2021

jidm

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The detection of events in time series is an important task in several areas of knowledge where operations monitoring is essential. Experts often have to deal with choosing the most appropriate event detection method for a time series, which can be a complex task. There is a demand for benchmarking different methods in order to guide this choice. For this, standard classification accuracy metrics are usually adopted. However, they are insufficient for a qualitative analysis of the tendency of a method to precede or delay event detections. Such analysis is interesting for applications in which tolerance for "close" detections is important rather than focusing only on accurate ones. In this context, this paper proposes a more comprehensive event detection benchmark process, including an analysis of temporal bias of detection methods. For that, metrics based on the time distance between event detections and identified events (detection delay) are adopted. Computational experiments were conducted using real-world and synthetic datasets from Yahoo Labs and resources from the Harbinger framework for event detection. Adopting the proposed detection delay-based metrics helped obtain a complete overview of the performance and general behavior of detection methods.

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Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model

Cited by 128 publications

References 5 publications

Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification

Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification

The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic

Evaluating Temporal Bias in Time Series Event Detection Methods

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