Prediction of COVID-19 disease severity using machine learning techniques

Ahmed, Alaa H.; Al-Hamadani, Mokhaled N. A.; Satam, Ihab A.

doi:10.11591/eei.v11i2.3272

Cited by 13 publications

(13 citation statements)

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“…After the process of detecting COVID-19 virus, AL and AI took the diagnosis stage into another level by using various machine learning and deep learning algorithms [19]. For instance, "Case Based Reasoning" is used in a research to propose an AI system that works by classifying the COVID-19 symptoms and giving the diagnosis result of each patient with the help of the doctors [20].…”

Section: Diagnosismentioning

confidence: 99%

Evolution of automated learning techniques for combating COVID-19: an analysis

Mohammed

Hamsheen

2022

IJEECS

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It is now more than two years that the world is battling the tiny invisible virus, COVID-19. Since its appearance, it showered humankind with shock, fear, and death. In small words, this pandemic has paused human life in all its aspects and beauties. Governments, health industry researchers and laboratories have put all their efforts to achieve a universal goal that is, overcoming the crisis and putting an end to the pandemic. However, this goal was never achievable without the smart use of automated learning, artificial intelligence, machine learning and deep learning algorithms. This review paper presents a collection of the experimental research articles tackled using real-time official datasets from hospitals and governments. These datasets are processed using automated learning (AL) algorithms in order to find suitable solutions to most of the COVID-19 related problems. This paper presents the AL applications in a story telling manner, starting from the first phases of COVID-19, when doctors had no experience dealing with the disease and had difficulty in diagnosing it, then moving to the other phases like suggesting a medicine, drug repurposing, facial mask detection, fake news detection, vaccine development, pandemic management, post vaccine statistics and lastly post COVID-19 analysis.

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Section: Diagnosismentioning

confidence: 99%

Evolution of automated learning techniques for combating COVID-19: an analysis

Mohammed

Hamsheen

2022

IJEECS

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“…Traditional machine learning branches can be specified as shown in Figure 1 . Supervised learning: This method involves learning from a training dataset labeled with desired results [ 8 , 9 ]. It is the most common learning approach in the machine learning field.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

Al-Hamadani,

Fadhel,

Alzubaidi

et al. 2024

Sensors

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Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential decision-making problems with simultaneous sampling, evaluation, and feedback. As a result, RL techniques have become suitable candidates for developing powerful solutions in various domains. In this study, we present a comprehensive and systematic review of RL algorithms and applications. This review commences with an exploration of the foundations of RL and proceeds to examine each algorithm in detail, concluding with a comparative analysis of RL algorithms based on several criteria. This review then extends to two key applications of RL: robotics and healthcare. In robotics manipulation, RL enhances precision and adaptability in tasks such as object grasping and autonomous learning. In healthcare, this review turns its focus to the realm of cell growth problems, clarifying how RL has provided a data-driven approach for optimizing the growth of cell cultures and the development of therapeutic solutions. This review offers a comprehensive overview, shedding light on the evolving landscape of RL and its potential in two diverse yet interconnected fields.

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“…The use of machine learning and creating artificial intelligence models and methods is very important in classifying, clustering and performing other prediction models [19]. The use of machine learning in many cases and datasets in the field of health has contributed a lot of knowledge [20]. Today and in the future, algorithms in processing datasets are becoming increasingly necessary knowledge in many fields [21].…”

Section: Introductionmentioning

confidence: 99%

Prediction of linear model on stunting prevalence with machine learning approach

Mambang

Marleny²,

Zulfadhilah³

2023

Bulletin EEI

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An increase in the number of residents should be anticipated including in the health sector, especially the problem of stunting. Stunting in children disrupts height and lack of absorption of nutrients. Information and data drive change in many areas such as health, entertainment, economics, business, and other strategic areas. The stages carried out in this study are initiating, developing linear models, and making prediction results on linear machine learning models. The results of testing with the scikit-learn linear model with a minimum variable of 19 get the best test results, namely the polynomial regression with pipeline model with mean absolute percentage error (MAPE) 0.02, root mean square error (RMSE) 3.32, and coefficient of determination (R2) 1,00. Testing with the scikit-learn linear model with a maximum variable of 48 gets the best test results, namely the polynomial regression with pipeline model with MAPE 0.00, RMSE 3.79 and R2 1.00. Testing with the scikit-learn linear model with an average variable of 32 gets the best test results, namely the polynomial regression model with MAPE 0.01, RMSE 3.32, and R2 1.00. The results of testing with the scikit-learn linear model with the minimum, maximum, and average variables get the best test results, namely the polynomial regression with pipeline model.

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Prediction of COVID-19 disease severity using machine learning techniques

Cited by 13 publications

References 0 publications

Evolution of automated learning techniques for combating COVID-19: an analysis

Evolution of automated learning techniques for combating COVID-19: an analysis

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

Prediction of linear model on stunting prevalence with machine learning approach

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