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. 2023 Jul 24;10(7):880.
doi: 10.3390/bioengineering10070880.

Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection

Sekar Kidambi Raju  1 , Seethalakshmi Ramaswamy  2 , Marwa M Eid  3 , Sathiamoorthy Gopalan  2 , Faten Khalid Karim  4 , Raja Marappan  1 , Doaa Sami Khafaga  4
Affiliations

Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection

Sekar Kidambi Raju et al. Bioengineering (Basel). .

Abstract

This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can help with efficient planning and resource allocation. The proposed model utilizes stochastic regression to capture the virus transmission's stochastic nature, considering data uncertainties. Feature selection techniques are employed to identify the most relevant and informative features contributing to prediction accuracy. Furthermore, the study explores the use of neighbor embedding and Sammon mapping algorithms to visualize high-dimensional SARS-CoV-2 respiratory infection data in a lower-dimensional space, enabling better interpretation and understanding of the underlying patterns. The application of machine-learning techniques for predicting SARS-CoV-2 respiratory infections, the use of statistical measures in healthcare, including confirmed cases, deaths, and recoveries, and an analysis of country-wise dynamics of the pandemic using machine-learning models are used. Our analysis involves the performance of various algorithms, including neural networks (NN), decision trees (DT), random forests (RF), the Adam optimizer (AD), hyperparameters (HP), stochastic regression (SR), neighbor embedding (NE), and Sammon mapping (SM). A pre-processed and feature-extracted SARS-CoV-2 respiratory infection dataset is combined with ADHPSRNESM to form a new orchestration in the proposed model for a perfect prediction to increase the precision of accuracy. The findings of this research can contribute to public health efforts by enabling policymakers and healthcare professionals to make informed decisions based on accurate predictions, ultimately aiding in managing and controlling the SARS-CoV-2 pandemic.

Keywords: SARS-CoV-2 prediction; feature selection; machine learning; neighbor embedding; sammon mapping; stochastic regression.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Architecture of the proposed mode.
Figure 2
Figure 2
Structure of neural networks with deep connectivity shift-invariant convolution.
Figure 3
Figure 3
Feature selection procedure block diagram.
Figure 4
Figure 4
Top 10 countries with highest number of confirmed cases.
Figure 5
Figure 5
Death and recoveries over time.
Figure 6
Figure 6
Models with error rate.
Figure 7
Figure 7
Models with loss function.
Figure 8
Figure 8
Models with computational time.
Figure 9
Figure 9
ROC for neural network classifier.
Figure 10
Figure 10
ROC for decision tree classifier.
Figure 11
Figure 11
ROC for random forest classifier.
See this image and copyright information in PMC

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