A novel approach to predict COVID-19 using support vector machine

Guhathakurata, Soham; Kundu, Souvik; Chakraborty, Arpita; Banerjee, Jyoti Sekhar

doi:10.1016/b978-0-12-824536-1.00014-9

Cited by 47 publications

(13 citation statements)

References 13 publications

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“…The data-driven models formulate the prediction of the COVID-19 cases primarily as a regression problem and exploit fully data-adaptive approaches to understand the functional relationship between COVID-19 cases with a set of observable variables. Data-driven models include classical statistical models such as Autoregressive models (AR) [6][7][8] and Support Vector Regression (SVR) [9][10][11] , and deep learning models [12][13][14][15][16][17][18] . In this paper, we will focus on data-driven models.…”

Section: Introductionmentioning

confidence: 99%

An Interpretable Hybrid Predictive Model of COVID-19 Cases using Autoregressive Model and LSTM

Zhang

Tang

Guo

2022

Preprint

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The Coronavirus Disease 2019 (COVID-19) has posed a severe threat to global human health and economic. It is an urgent task to build reliable data-driven prediction models for Covid 19 cases to improve public policy making. However, COVID-19 data shows special transmission characteristics such as significant fluctuations and non-stationarity, which may be difficult to be captured by a single predictive model and poses grand challenges in effective forecasting. In this paper, we proposed a novel Hybrid data-driven model combining Autoregressive model (AR) and long short-term memory neural networks (LSTM). It can be viewed as a new neural network model and the contribution of AR and LSTM is auto tuned in the training procedure. We conduct extensive numerical experiments on data collected from 8 counties of California that display various trends. The numerical results show the Hybrid model' advantages over AR and LSTM by its predictive powers. We show that the Hybrid model achieved 4.195% MAPE, outperformed the AR 5.629% and LSTM 5.070% on average, and provide a discussion on interpretability.

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Section: Introductionmentioning

confidence: 99%

An Interpretable Hybrid Predictive Model of COVID-19 Cases using Autoregressive Model and LSTM

Zhang

Tang

Guo

2022

Preprint

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“…Since the outbreak of COVID-19, the virus has evolved at a tremendous rate. One of the major causes of the failure of COVID-19 detection ( Biswas et al, 2020 , Guhathakurata, Kundu, Chakraborty, & Banerjee, 2020 , Guhathakurata et al, 2020a , Guhathakurata et al, 2020b ) from the symptoms is mainly because of the uncertain nature of the virus. As a result of which no proper dataset is available to use as a reference.…”

Section: Proposed Covid-19 Detection Methodologymentioning

confidence: 99%

A new approach to predict COVID-19 using artificial neural networks

Guhathakurata¹,

Saha²,

Kundu

et al. 2022

Cyber-Physical Systems

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“…The reason for applying SVM in the proposed model is that SVM has been used in various studies and has achieved promising results for detecting COVID-19 (like in [29,43]) based on clinical blood results. Studies like [6,32,69] have used the SVM classifier and achieved promising results (like 80% accuracy, 81.4% accuracy, and 92.8% sensitivity, respectively) for predicting severity and mortality risks based on clinical blood tests.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Applying Hunger Game Search (Hgs) for Selecting Significant Blood Indicators for Early Prediction of Icu Covid-19 Severity

Sayed

El-Korany

Sayed

2023

csci

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Millions of people around the world have been affected and some have died during the global pandemic Corona (COVID-19). This pandemic has created a global threat to people's lives and medical systems. The constraints of hospital resources and the pressures on healthcare workers during this period are among the reasons for wrong decisions and medical deterioration. Anticipating severe patients is an urgent matter of resource consumption by prioritizing patients at high risk to save their lives. This paper introduces an early prognostic model to predict the severity of patients and detect the most significant features based on clinical blood data. The proposed model predicts ICU severity within the first 2 hours of hospital admission, seeks to assist clinicians in decision-making and facilitates efficient use of hospital resources. The Hunger Game Search (HGS) meta-heuristic algorithm and the SVM are hybridized for building the proposed prediction model. Furthermore, they have been used for selecting the most informative features from the blood test data. Experiments have shown that using HGS for selecting the features with the SVM classifier achieved excellent results compared with the other four meta-heuristic algorithms. The model using the features selected by the HGS algorithm accomplished the topmost results, 98.6% and 96.5% for the best and mean accuracy, respectively, compared with using all features and features selected by other popular optimization algorithms.

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A novel approach to predict COVID-19 using support vector machine

Cited by 47 publications

References 13 publications

An Interpretable Hybrid Predictive Model of COVID-19 Cases using Autoregressive Model and LSTM

An Interpretable Hybrid Predictive Model of COVID-19 Cases using Autoregressive Model and LSTM

A new approach to predict COVID-19 using artificial neural networks

Applying Hunger Game Search (Hgs) for Selecting Significant Blood Indicators for Early Prediction of Icu Covid-19 Severity

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