Machine learning approaches in Covid-19 severity risk prediction in Morocco

Abstract

The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.

Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques

“…Likewise, there is a great variety of studies of this type despite the short time that has elapsed since the pandemic began [ 16 , 41 ]. We can find research around the world about this topic employing IA techniques such as random forest models [ 17 , 42 , 43 , 44 , 45 ], deep learning [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ], decision trees [ 43 , 54 ], support vector machine (SVM) [ 49 , 55 ] and logistic regression procedures [ 49 , 56 ]; which are intended to predict the health status (mortality risk or disease severity) of a COVID-19 infected patient employing factors such as the patients age, weight, gender, physiological conditions, demographic data, travel data, computed tomography, vital signs, symptoms, smoking history, radiological features, clinical features, genetic variants, platelets, laboratory test, D-dimer test, chronic comorbidities and general health information. Meantime, other studies [ 57 ] create models using data analysis techniques with the aim of predicting the need of oxygen therapy in a timely manner in COVID-19 patients; which employed variables like shortness of breath, cough, age and fever.…”

COVID-19 Mortality Risk Prediction using Clinical and Laboratory Examination: Machine Learning Approach for Implementation

“…Feature selection methods dismissed parameters with a high level of correlations and collinearity. In previous studies, laboratory markers, patient demographics, medical history, and vital signs have been used as effective features in predicting the mortality of patients with COVID-19 (7,13,(17)(18)(19)(20)(21)(22). Some studies used factors including different in ammatory cytokines (23)(24)(25)(26), which are not part of patients' routine admission measurements and cannot be obtained in settings with congested resources in contrast to our predictors.…”

Determining latent features and forecasting of COVID-19 hospitalisations in Malaysia using a national patient assessment data platform: a study of machine learning modelling against expert system

“…Random Forest (RF), and ensemble-based ML algorithms, has been applied to predict the severity and outcome of COVID-19 patients [10][11][12][13][14][15]. Using a collection of decision trees, RF, a type of machine learning, can assess intricate relationships between clinical traits and offer highly accurate classifications [16].…”