Machine Learning Approaches in COVID-19 Diagnosis, Mortality, and Severity Risk Prediction: A Review.

Background: Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction.
Objective: Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes.
Methods: We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods.
Results: Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19–positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction.
Conclusions: We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.

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Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development.

Introduction: For COVID-19 patients accurate prediction of disease severity and mortality risk would greatly improve care delivery and resource allocation. There are many patient-related factors, such as pre-existing comorbidities that affect disease severity. Since rapid automated profiling of peripheral blood samples is widely available, we investigated how such data from the peripheral blood of COVID-19 patients might be used to predict clinical outcomes. 
Methods: We thus investigated such clinical datasets from COVID-19 patients with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, K-nearest neighbour and deep learning methods. 
Results: Our work revealed several clinical parameters measurable in blood samples, which discriminated between healthy people and COVID-19 positive patients and showed predictive value for later severity of COVID-19 symptoms. We thus developed a number of analytic methods that showed accuracy and precision for disease severity and mortality outcome predictions that were above 90%. 
Conclusions: In sum, we developed methodologies to analyse patient routine clinical data which enables more accurate prediction of COVID-19 patient outcomes. This type of approaches could, by employing standard hospital laboratory analyses of patient blood, be utilised to identify, COVID-19 patients at high risk of mortality and so enable their treatment to be optimised.

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Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Blood Cell Transcriptome Data.

https://doi.org/10.1016/j.artmed.2022.102286

Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review

Image fusion has gained huge popularity in the field of medical and satellite imaging for image analysis. The lack of usages of image fusion is due to a deficiency of suitable optimization techniques and dedicated hardware. In recent days WOA (whale optimization algorithm) is gaining popularity. Like another straightforward nature-inspired algorithm, WOA has some problems in its searching process. In this paper, we have tried to improve the WOA algorithm by modifying the WOA algorithm. This MWOA (modified whale optimization algorithm) algorithm is amalgamed with LSA (local search algorithm) and BA (bat algorithm). The LSA algorithm helps the system to be faster, and BA algorithm helps to increase the accuracy of the system. This optimization algorithm is checked using MATLAB R2018b. Simulated using ModelSim, and the synthesizing is done using Xilinx Vivado 18.2 synthesis tool. The outcome of the simulation result and the synthesis result outshine other metaheuristic optimization algorithms.

https://www.irojournals.com/jscp/V2/I4/01.pdf

Highly Precise Modified Blue Whale Method Framed by Blending Bat and Local Search Algorithm for the Optimality of Image Fusion Algorithm

Ensemble learning model for diagnosing COVID-19 from routine blood tests

The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.

/pdf/deep-forest-model-for-diagnosing-covid-19-from-routine-blood-1nfrc5idhe.pdf

Deep forest model for diagnosing COVID-19 from routine blood tests.

Population-based meta-heuristic algorithms are among the dominant algorithms used to solve challenging real world problems in diverse fields. Whale Optimization Algorithm (WOA) is a recent swarm intelligence meta-heuristic algorithm based on the bubble-net feeding behavior of humpback whales. Despite its capability to solve complex optimization problems, WOA requires enormous amount of computations when solving large size problems. This work proposes Spark-WOA, a distributed implementation of WOA on Apache Spark platform to enhance its performance and reduce computational complexity. The proposed algorithm exploits in-memory computations and broadcast features of Apache Spark to provide better performance and scalability. Details of the proposed algorithm are presented and its performance as compared to a recent Apache Hadoop implementation is discussed. Experimental results demonstrated the superiority of the proposed implementation in terms of both speed and scalability.

Apache Spark Implementation of Whale Optimization Algorithm

The Sine Cosine Algorithm (SCA) has experienced wide spread use in solving optimization problems in many disciplines mainly due to its simplicity and efficiency. However, like many other meta-heuristics, SCA requires considerable amount of compute time when solving large size optimization problems. Therefore, in order to tackle such challenging problems efficiently, this work proposes Spark-SCA, a scalable and parallel implementation of SCA algorithm on Apache Spark distributed framework. Spark-SCA exploits Spark platform native support for iterative algorithms through in-memory computing to speed-up computations when handling large optimization problems. Both the design and implementation details of Spark-SCA are presented herein. The performance of Spark-SCA was compared to standard SCA on different benchmark functions with up to 1,000-dimension as well as three practical engineering design problems. Simulation experiments conducted on Amazon Web Services (AWS) public cloud demonstrated how Spark-SCA outperforms the standard version in terms of solution quality and run time as well as it competitiveness in exploring solution space of complex optimization problems.

/pdf/parallel-and-distributed-implementation-of-sine-cosine-saoph43ld9.pdf

Maryam AlJame

Papers

Ensemble learning model for diagnosing COVID-19 from routine blood tests

Deep forest model for diagnosing COVID-19 from routine blood tests.

Apache Spark Implementation of Whale Optimization Algorithm

Parallel and Distributed Implementation of Sine Cosine Algorithm on Apache Spark Platform

DNA short read alignment on apache spark