Impact of treatment protocols on hospital length of stay for COVID-19 patients: A machine learning analysis of cases in Khuzestan province, Iran.

Objective

COVID-19 has heavily burdened healthcare systems worldwide, underscoring the need for accurate treatment decision-making to optimize patient recovery. This study leverages machine learning (ML) to evaluate how treatments affect the length of stay (LOS) for hospitalized COVID-19 patients in Iran. Method: We analyzed clinical data from 1793 patients with 106 features, identifying key variables through detailed profiles. Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Artificial Neural Network (ANN) models were then used to predict LOS based on personalized COVID-19 treatment regimens.

Results

Actemra and Bromhexine exhibited the strongest correlation with LOS. In the first experiment, the models achieved average predictive accuracies of 90.0% (SVM), 89.53% (k-NN), and 86.30% (ANN); in the second experiment, the accuracies were 96.8% (SVM), 89.53% (k-NN), and 94.56% (ANN), demonstrating their effectiveness in forecasting hospital stay durations.

Conclusion

Our study showed that medications such as Actemra and Bromhexine were associated with the affected factors for predicting LOS, especially when administered early to patients without major comorbidities. Those with conditions such as cardiovascular disease or diabetes had longer stays. The ML models predicted LOS with high accuracy, demonstrating their potential to assist clinical decisions. Overall, early treatment and predictive modeling can enhance patient outcomes and optimize hospital resource use.