Smart Healthcare Decision Making with Bi-LSTM for COVID-19 Detection and ICU Prediction

Problem Definition

The existing literature on AI-based approaches for the detection of COVID-19 in humans highlights several limitations and challenges that researchers have encountered. While machine learning (ML) algorithms have shown promise in accurately predicting COVID-19, they struggle with handling large datasets, leading to decreased efficiency in the detection system. The complexity of current ML-based systems is another issue, as not enough emphasis has been placed on reducing the dimensionality of the datasets. Additionally, many ML algorithms used by researchers face challenges such as getting stuck in local minima or having high computational costs. Furthermore, feature selection, which is crucial for enhancing system accuracy, has been overlooked in these approaches.

To address these limitations and improve the overall performance of COVID-19 detection systems, a new model utilizing deep learning methods is recommended. This proposed model aims to enhance accuracy, reduce system complexity, and lower computational costs, ultimately leading to more efficient and effective COVID-19 detection.

Objective

The objective of this research is to address the limitations of current COVID-19 detection models by proposing a new model based on deep learning methods. The main goal is to reduce system complexity, enhance accuracy, and lower computational costs in order to improve the efficiency and effectiveness of COVID-19 detection. This proposed model will focus on two classification phases: identifying COVID-19 in patients and predicting the necessity for ICU/semi-ICU requirements. By applying advanced techniques such as Eigenvector centrality Feature Selection (ECFS) and Bi-LSTM, the aim is to preprocess and analyze the dataset effectively, handle large datasets efficiently, reduce dimensionality, and optimize system performance for accurate predictions.

Proposed Work

In order to overcome the limitations of traditional Covid-19 detection models, a new and enhanced detection model that is based on DL method is proposed in this research. The suggested method works for two classification phases, the first phase is intended for identifying covid-19 in patients and appropriately the necessity for ICU/semi-ICU requirement if predicted in the second phase. The main objective of the proposed DL method is to reduce the complexity of the system as well as enhance the accuracy of the system. To accomplish this task, firstly a dataset is needed upon which more advanced techniques will be applied to generate the final covid-19 and ICU requirement predictions. However, the problem with the available datasets is that they are unbalanced in nature and contain a lot of empty cells, null and NAN values, which enhances the complexity of the system.

Therefore, it becomes necessary to apply pre-processing and other advanced techniques to it so that its complexity id reduced and only informative and useful data is present in it. Here, we propose an efficient and effective method where, Eigenvector centrality Feature Selection (ECFS) technique is applied along with the advanced version of LSTM, named as, Bi-LSTM (bidirectional Long Short-Term Memory). The main motive for using the Bi-LSTM is that it can handle large datasets effectively and also it remembers the information of the pasta as well as the future. Along with this, the feature selection technique used helps in reducing the dimensionality of the dataset which in return reduces the overall complexity and increases the accuracy of the system.

Application Area for Industry

This project can be used in various industrial sectors such as healthcare, pharmaceuticals, and biotechnology. In the healthcare industry, the proposed DL method can effectively detect and predict COVID-19 in patients, helping in timely and accurate diagnosis. The use of advanced techniques like Bi-LSTM and ECFS can help in reducing the complexity of the system and improving the accuracy of predictions. In the pharmaceutical and biotechnology sectors, this project can aid in drug discovery and development by providing accurate insights into the disease and its impact on patients. By addressing the challenges of large datasets, complexity, and computational cost, the proposed solutions can bring significant benefits to these industries, leading to improved efficiency and effectiveness in COVID-19 detection and prediction.

Application Area for Academics

The proposed project can greatly enrich academic research, education, and training in the field of healthcare and AI. By developing a new and enhanced Covid-19 detection model based on deep learning methods, researchers can explore innovative research methods, simulations, and data analysis within educational settings. This project can provide a practical application for researchers, MTech students, and PHD scholars in the healthcare domain, allowing them to utilize the code and literature for their own work. The relevance of this project lies in addressing the limitations of traditional ML-based Covid-19 detection models, such as handling large datasets, reducing complexity, and improving accuracy. The use of advanced techniques like Eigenvector centrality Feature Selection (ECFS) and Bi-LSTM can enhance the system's efficiency and performance.

Researchers can benefit from this project by exploring new methods for disease detection and prediction, while students can gain valuable insights into deep learning algorithms and feature selection techniques. The potential applications of this project extend to various research domains, particularly in healthcare and AI. By focusing on Covid-19 detection and ICU requirement prediction, researchers can contribute to the ongoing efforts to combat the pandemic. MTech students and PHD scholars can leverage the code and literature of this project to enhance their own research projects, leading to further advancements in the field. In the future, this project has the potential to be expanded to other disease detection systems and healthcare applications.

By incorporating additional deep learning algorithms and feature selection techniques, researchers can further improve the accuracy and efficiency of diagnostic systems. Overall, this project offers a valuable opportunity for academic institutions to engage in cutting-edge research and training in the intersection of healthcare and AI.

Algorithms Used

In the proposed DL method for Covid-19 detection, the ECFS (Eigenvector centrality Feature Selection) technique is used to select the most informative features from the dataset. This helps in reducing the dimensionality of the data and improving the overall efficiency of the system by focusing on relevant information. Bi-LSTM (bidirectional Long Short-Term Memory) is utilized as the DL model for classification, as it can effectively handle large datasets and remember both past and future information. By using Bi-LSTM, the model aims to improve accuracy in predicting Covid-19 diagnosis and the need for ICU/semi-ICU requirements. These algorithms collectively contribute to enhancing the accuracy of the system and reducing complexity, resulting in a more effective Covid-19 detection model.

Keywords

SEO-optimized keywords: COVID-19 detection, DL method, ML algorithms, deep learning, dataset complexity, feature selection, Bi-LSTM, LSTM, ICU requirement prediction, Eigenvector centrality Feature Selection, advanced techniques, unbalanced datasets, pre-processing, medical imaging, disease classification, pneumonia detection, radiology, computer-aided diagnosis, COVID-19 screening, chest X-ray images, image classification, deep neural networks, COVID-19 classification, image-based diagnosis, convolutional neural networks.

SEO Tags

COVID-19 classification, chest X-ray images, deep neural networks, medical image analysis, computer-aided diagnosis, image classification, COVID-19 detection, deep learning, convolutional neural networks, COVID-19 screening, medical imaging, disease classification, pneumonia detection, radiology, image-based diagnosis, ML algorithms, DL method, LSTM, Bi-LSTM, Eigenvector centrality Feature Selection, dataset preprocessing, ICU requirement prediction, unbalanced datasets.