Predicting COVID-19 patient recovery or mortality using deep neural decision tree and forest

Among the models, the deep neural decision forest outperformed others. Results indicated that using only clinical data yielded an accuracy of 80.7%, recall of 80.7%, precision of 75.7%, and F1-score of 74.8% by deep neural decision forest, demonstrating it as a reliable predictor of patient mortality. The model differs from other machine learning approaches for COVID-19 mortality prediction by combining the representational power of deep neural networks with the structured decision-making of decision forests, enhancing interpretability and performance using only clinical data without reliance on imaging or laboratory tests.