ArticleJournal of infection and public health
Main factors influencing recovery in MERS Co-V patients using machine learning.
Article in Journal of infection and public health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.Journal of medical systems · 2020Pooled it
- A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings.Health science reports · 2024Review
- Novel deep learning approach to model and predict the spread of COVID-19.Intelligent systems with applications · 2022Article
- Global Trends in Natural Biopolymers in the 21st Century: A Scientometric Review.Frontiers in chemistry · 2022Review
- Health Information Technology During the COVID-19 Epidemic: A Review via Text Mining.Online journal of public health informatics · 2022Article
- The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype.Frontiers in artificial intelligence · 2021Review
- Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review.Journal of medical systems · 2020Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMiddle East Respiratory Syndrome (MERS) is a major infectious disease which has affected the Middle Eastern countries, especially the Kingdom of Saudi Arabia (KSA) since 2012. The high mortality rate associated with this disease has been a major cause of concern. This paper aims at identifying the major factors influencing MERS recovery in KSA.
methodsThe data used for analysis was collected from the Ministry of Health website, KSA. The important factors impelling the recovery are found using machine learning. Machine learning models such as support vector machine, conditional inference tree, naïve Bayes and J48 are modelled to identify the important factors. Univariate and multivariate logistic regression analysis is also carried out to identify the significant factors statistically.
resultThe main factors influencing MERS recovery rate are identified as age, pre-existing diseases, severity of disease and whether the patient is a healthcare worker or not. In spite of MERS being a zoonotic disease, contact with camels is not a major factor influencing recovery.
conclusionThe methods used were able to determine the prime factors influencing MERS recovery. It can be comprehended that awareness about symptoms and seeking medical intervention at the onset of development of symptoms will make a long way in reducing the mortality rate.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.