Predicting Patient Recovery or Mortality Using Deep Neural Decision Tree and Forest

Objective: Identifying patients at high risk of mortality is crucial for emergency physicians to allocate hospital resources effectively, particularly in regions with limited medical services. This need becomes even more pressing during global health crises that lead to significant morbidity and mortality. This study aimed to present the usability deep neural decision forest and deep neural decision tree to predict mortality among Coronavirus disease 2019 (COVID-19) patients. To this end, We used patient data encompassing Coronavirus disease 2019 diagnosis, demographics, health indicators, and occupational risk factors to analyze disease severity and outcomes. The dataset was partitioned using a stratified sampling method, ensuring that 80% was allocated for training and 20% for testing. Nine machine learning and deep learning methods were employed to build predictive models. The models were evaluated across all stages to determine their effectiveness in predicting patient outcomes. Results: Among the models, the deep neural decision forest consistently outperformed others. Results indicated that using only clinical data yielded an accuracy of 80% by deep neural decision forest, demonstrating it as a reliable predictor of patient mortality. Moreover, the results suggest that clinical data alone may be the most accurate diagnostic tool for predicting mortality.