Main factors influencing recovery in MERS Co-V patients using machine learning

Abstract

a b s t r a c tBackground: Middle 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. Methods: The 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. Result: The 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. Conclusion: The 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.

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

“…The experimental results indicated that age and symptoms are the two dominant features for the prediction model and that healthcare staff are likely to survive. In [12], a study was conducted in Saudi Arabia to identify the dominant factors that influence human infection using statistical methods, such as univariate and multivariate regression methods. The results indicated four dominant features, namely, disease severity, patient age, the patient job as a healthcare staff or not and history of chronic disease.…”

“…The sample size in [4] represents 1082 records of cases reported from 2013 to 2015 distributed as 633 new case records, 231 recovery records and 218 death records. The study in [12] includes 836 patient records, and 52 patients are reported as dead and only 784 cases are used. In [13], the sample size is represented by articles collected from the Internet and reported by 153 news media outlets in Korea and the comments associated with these articles from day 1 (first confirmed case on May 20, 2015) to the day 70.…”

The Role of Electronic Health in the Coronavirus Disease Crisis: A Systematic Review of Documents

“…These results indicate that scholars are well aware of the potential and usefulness of these algorithms and conducting research on intellectual systems. For example, the results of this review showed that in 2018, machine learning algorithms and artificial intelligence can be promising in helping patients in different areas of healthcare [50], such as the use of machine learning algorithms and artificial intelligence in treating viral epidemic diseases like MERS and Ebola [51][52][53][54]. On the other hand, one of the most salient challenges for smart systems is the problem of knowledge acquisition; for instance, in designing a Neural Network the number of samples required to educate the system is one of the main issues.…”

Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review

“…Machine learning algorithms have been used previously to predict prognosis in patients affected by the MERS Co-V infection [50]. The patient's age, disease severity on presentation to the healthcare facility, whether the patient was a healthcare worker, and the presence of pre-existing co-morbidities were the four factors that were identified to be the major predictors in the patient's recovery.…”