Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms

Background: The severity of coronavirus in patients with chronic comorbidities is much higher than in other patients, which can lead to their death. Machine learning (ML) algorithms as a potential solution for rapid and early clinical evaluation of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. Objective: To predict the mortality risk and length of stay (LoS) of patients with COVID-19 and history of chronic comorbidities using ML algorithms. Methods: This retrospective study reviewed medical records of COVID-19 patients with chronic comorbidities (Afzalipour Hospital, Kerman, Iran; March 2020–January 2021). Outcomes were discharge (alive) or death, and LoS. Filter-based feature scoring and well-known ML algorithms (including ensemble methods) were used to predict mortality risk and LoS. Performance was evaluated using F1, precision, recall, accuracy, and ROC/AUC; reporting followed TRIPOD. Results: 1291 patients (900 alive, 391 dead) were analyzed. Common symptoms were shortness of breath (53.6%), fever (30.1%), and cough (25.3%). Common comorbidities were diabetes mellitus (DM; 31.3%), hypertension (HTN; 27.3%), and ischemic heart disease (IHD; 14.2%). Twenty-six key factors were extracted. Gradient boosting achieved the best mortality prediction accuracy among ensembles (accuracy 84.15%); SVM performed best among base classifiers (accuracy 80.00%, ROC 0.85). For LoS prediction, a multilayer perceptron (MLP) with ReLU activation performed best (MSE 38.96). The most important factors for mortality included hyperlipidemia, diabetes, asthma, and cancer; shortness of breath was highlighted for LoS. Conclusion: ML algorithms can effectively predict mortality risk and LoS for COVID-19 patients with chronic comorbidities using physiological, symptomatic, and demographic data. Gradient boosting and MLP can rapidly identify patients at risk of death or prolonged hospitalization to inform timely clinical interventions.