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.
【저자키워드】 COVID-19, severity, machine learning, Data analysis, Feature selection, Feature reduction, 【초록키워드】 hospital, COVID-19 severity, C-reactive protein, D-dimer, Platelets, sensitivity, specificity, Characteristics, Accuracy, severity of COVID-19, Algorithm, Research, prognostic, Admission, predict, UMAP, COVID-19 test, ROC Curve, Illness severity, Morocco, reduction, patient data, Sickness, projection, forest, AdaBoost, approximation, topological data analysis, approach, shown, develop, approach, were used, assist, COVID-19 positive patient, machine learning classifier, Manifold, patients with COVID-19, therapeutic purpose, Uniform, Zaid, 【제목키워드】 risk, Morocco, machine, approach,