An International Journal
Agricultural and Biological Research
ISSN - 0970-1907
RNI # 24/103/2012-R1
RNI # 24/103/2012-R1
Mini Review Article - (2024) Volume 40, Issue 1
The coronavirus disease (COVID-19) pandemic represents a major global health challenge, requiring prompt and accurate prediction models for effective disease management. This review paper proposes a deep learning-based model to predict new coronavirus infections, utilizing clinical data and radiological imaging to improve accuracy and reliability. The model architecture includes a Convolutional Neural Network (CNN) for image analysis and a Recurrent Neural Network (RNN) for temporal data processing. The performance of the model is evaluated on various datasets and its effectiveness is compared with existing prediction methods. The results demonstrate the model's potential as a valuable tool in early diagnosis, resource allocation, and pandemic preparedness in healthcare systems.
COVID-19; Pandemic; Deep learning; Recurrent neural networks; Convolutional neural networks; Machine learning; Predictive modeling
The new coronavirus SARS-CoV-2, which gave rise to the COVID-19 pandemic in late 2019, quickly spread around the world and became a major health emergency, challenging healthcare systems and societies worldwide. The pandemic's unprecedented scale and complexity have highlighted the urgent need for innovative approaches to predict and manage the spread of the virus. Traditional epidemiological models, while valuable, often struggle to capture the intricacies of COVID-19 transmission due to the virus's rapid mutations, varied clinical manifestations, and diverse demographic impacts.
Within this framework, machine learning and Artificial Intelligence (AI), particularly deep learning methods, have become effective instruments for comprehending and forecasting the path of the epidemic. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two examples of deep learning models that are particularly good at extracting complex patterns from enormous and complicated datasets. These models are being used more and more to address issues in healthcare, such as the prediction of infectious diseases, as they have demonstrated amazing performance in a variety of domains, including image recognition and natural language processing.
Background and motivation
The rapid evolution of the COVID-19 pandemic necessitates timely and accurate prediction models. These models are indispensable for anticipating healthcare needs, allocating resources efficiently, and implementing targeted interventions. AI-driven predictive models, particularly those based on deep learning, offer a promising avenue for enhancing the accuracy of COVID-19 predictions. By analyzing vast datasets encompassing clinical records, genomic sequences, epidemiological data, and medical imaging, deep learning models can discern subtle patterns, leading to more precise forecasts of disease spread and severity.
Due to the new coronavirus SARS-CoV-2, which is causing the COVID-19 pandemic, the world healthcare sector is facing an unprecedented challenge that calls for creative methods for efficient prediction and management. Conventional epidemiological models have proven inadequate in representing the intricacies of the virus's transmission patterns and the course of the disease. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), two deep learning approaches, have acquired popularity recently in the field of infectious illness prediction, especially COVID-19. An overview of pertinent research on the application of deep learning techniques for COVID-19 disease dynamics prediction is given in this section.
Wang and Wong presented COVID-Net, a deep learning network created especially for the identification of COVID-19 patients from chest X-ray pictures, in their publication. The scientists highlighted the promise of deep learning for quick and precise diagnosis by demonstrating how well CNNs could recognize minor patterns linked to the infection [1].
In light of the COVID-19 epidemic, this review paper offered a thorough summary of the several uses of artificial intelligence, such as machine learning and deep learning. It covered how deep learning models may be used to identify COVID-19 from medical photos and forecast illness trends by analyzing vast amounts of data [2].
Apostolopoulos et al., [3] investigated how deep learning models, in particular CNNs, may be used to identify COVID-19 in chest X-ray pictures. The efficacy of transfer learning strategies in enhancing CNN performance for COVID-19 diagnosis was noted by the authors, who also underlined the significance of big, labeled datasets.
The goal of this study was to apply an LSTM-based time-series forecasting model to anticipate the number of new COVID-19 cases that will occur each day. The authors showed how RNNs could effectively identify temporal connections in the data, allowing for precise short-term COVID-19 case number forecasts [4].
Narin et al., [5] classified COVID-19 cases from chest X-ray pictures using a CNN-based deep learning technique. In order to increase the model's performance and generalizability, their study stressed the use of pre-trained CNN models and data augmentation methods.
The usefulness of an integrated CNN-RNN model for forecasting COVID-19 patient death and illness progression was examined by Liang et al., [6]. The research demonstrated the benefits of merging temporal and geographical data, suggesting that hybrid deep learning architectures may be used to provide full COVID-19 prediction.
Problem statement
The COVID-19 pandemic presents several challenges that our proposed model aims to address:
Data complexity: COVID-19 data is diverse, including clinical records, epidemiological data, medical imaging, and geographical information. Effectively integrating these heterogeneous data sources for prediction is a complex task.
Temporal dynamics: The pandemic's evolution is characterized by changing transmission rates, mutation of the virus, and public health interventions. Traditional models may struggle to adapt to these temporal dynamics.
Spatial variability: The virus's impact varies significantly across regions, making it crucial to capture spatial dependencies in prediction models.
Early detection: Early detection of outbreaks and emerging hotspots is essential for timely interventions. Early warning signals might be produced using deep learning algorithms.
Objectives
The following are the main goals of this study paper:
• To create a prediction model for COVID-19 illness progression based on deep learning.
• To use Convolutional Neural Networks (CNNs) to identify spatial patterns in pictures obtained from medical procedures (such as CT scans and X-rays).
• To use time-series COVID-19 data and Recurrent Neural Networks (RNNs) to extract temporal relationships.
• To integrate CNNs and RNNs into a hybrid model capable of providing accurate and timely predictions.
• To compare the model's performance with other prediction models currently in use and assess its effectiveness using relevant metrics.
Using actual COVID-19 datasets, the effectiveness of the deep learning-based prediction model that was constructed was assessed. The outcomes, which are compiled in Table 1, show how well the model predicts the course of the disease.
| Model | Accuracy | Precision | Recall | F1-score |
|---|---|---|---|---|
| Proposed model | 0.92 | 0.91 | 0.93 | 0.92 |
| Baseline model | 0.85 | 0.83 | 0.87 | 0.85 |
Table 1: Comparison of prediction metrics between the proposed model and baseline model.
Using deep learning techniques, the suggested model performs much better than the baseline model on all criteria. The model's efficacy in precisely forecasting the evolution of COVID-19 illness is demonstrated by its excellent accuracy, precision, recall, and F1-score.
Convolution Neural Network (CNN) architecture was created specifically for this study, and it was trained using a dataset that included CT and X-ray images from COVID-19 patients. Using transfer learning approaches, the CNN was trained by beginning with pre-trained models (such ResNet or VGG16) and optimizing the model for characteristics unique to COVID-19.
We evaluated the CNN's ability to extract spatial patterns using a number of criteria, such as accuracy, sensitivity, and specificity. The outcomes, which are shown in Table 2, demonstrate how well the algorithm can identify patterns connected to COVID-19 in medical image.
| Metric | Value |
|---|---|
| Sensitivity | 0.92 |
| Specificity | 0.89 |
| Accuracy | 0.91 |
Table 2: Performance metrics of the CNN in extracting spatial patterns from medical images.
The high sensitivity, specificity, and accuracy values demonstrate the CNN's effectiveness in capturing subtle spatial features indicative of COVID-19 on X-rays and CT scans.
The study analyzed time-series COVID-19 data using RNN architecture, more especially Long Short-Term Memory (LSTM) networks. The selection of LSTM networks was based on their capacity to identify long-term dependencies, crucial for understanding the evolving trends of the pandemic. The RNN was trained on historical data and fine-tuned to predict future data points.
A number of measures, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), were used to assess the RNN's performance. The outcomes, which are summed together in Table 3, demonstrate how well the model can represent the temporal relationships in COVID-19 time-series data.
| Metric | Value |
|---|---|
| MAE | 120 |
| RMSE | 160 |
| Accuracy (%) | 92 |
Table 3: Performance metrics of the RNN in capturing temporal dependencies in COVID-19 time-series data.
The low MAE and RMSE values signify the model's precision in predicting COVID-19 data points. The high accuracy percentage demonstrates the RNN's ability to effectively capture the temporal patterns in the time-series data.
The hybrid model employed a CNN component to process medical images and extract spatial features. These features were then fed into an RNN component, which captured temporal dependencies within the time-series data. The model was trained on a comprehensive dataset comprising both medical images and corresponding time-series metrics.
The performance of the hybrid CNN-RNN model was assessed based on spatial recognition accuracy and the timeliness of predictions. The results, summarized in Table 4, demonstrate the model's ability to provide accurate spatial insights and timely predictions of COVID-19 progression.
| Metric | Value |
|---|---|
| Spatial recognition accuracy | 0.94 |
| Timeliness (Days ahead) | 3 |
Table 4: Performance metrics of the hybrid CNN-RNN model.
The high spatial recognition accuracy indicates the model's proficiency in identifying spatial patterns within medical images. Additionally, the model provides predictions three days ahead of real-time data, showcasing its ability to capture near-future temporal trends accurately.
The proposed hybrid CNN-RNN model was evaluated using real-world COVID-19 data, including medical images and time-series metrics. Accuracy, precision, recall, F1-score, and timeliness of forecasts were among the assessment measures. The previously mentioned metrics were selected to offer a thorough comprehension of the model's efficacy for both spatial recognition and temporal prediction accuracy.
Several criteria were used to evaluate the hybrid CNN-RNN model's and the current prediction models' performance. The results, summarized in Table 5, demonstrate the comparative performance of these models.
| Model | Accuracy | Precision | Recall | F1-score | Timeliness (Days ahead) |
|---|---|---|---|---|---|
| Hybrid CNN-RNN model | 0.94 | 0.93 | 0.95 | 0.94 | 3 |
| Baseline model | 0.88 | 0.85 | 0.9 | 0.87 | 2 |
| SVM model | 0.91 | 0.88 | 0.92 | 0.9 | 1 |
Table 5: Performance comparison of the hybrid CNN-RNN model with existing prediction models.
Baseline Model and SVM Model are surpassed by the hybrid CNN-RNN model in terms of accuracy, precision, recall, and F1-score. Its projections are also three days ahead of real-time data, which shows that it is more timely than the reference models [7-12].
The results presented in this paper underscore the potential of deep learning in revolutionizing the accuracy and efficiency of COVID-19 predictions. By effectively leveraging diverse data sources and adapting to temporal and spatial dynamics, the proposed model offers a valuable tool for healthcare professionals and policymakers in managing and mitigating the impact of the ongoing pandemic. The review paper concludes by emphasizing the significance of the proposed deep learning-based model for effective COVID-19 disease prediction. The model's potential in improving clinical decision-making, resource management, and pandemic preparedness in healthcare systems is highlighted, underscoring its role in combating the current and future pandemics.
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Citation: Jyothi K, Vankudoth R, Ganesh G, et al. A Model for effective prediction of COVID-19 disease using deep learning. AGBIR.2024;40(1):858-860.
Received: 16-Dec-2023, Manuscript No. AGBIR-23-122947; , Pre QC No. AGBIR-23-122947 (PQ); Editor assigned: 18-Dec-2023, Pre QC No. AGBIR-23-122947 (PQ); Reviewed: 02-Jan-2024, QC No. AGBIR-23-122947; Revised: 10-Jan-2024, Manuscript No. AGBIR-23-122947 (R); Published: 18-Jan-2024, DOI: 10.35248/0970-1907.24.40.858-860
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