Predicting patients' length of hospital stay

Several factors can contribute to an extended length of stay for patients, such as comorbidities, severity of illness, chronic conditions, and the need for specialized treatment. Understanding the duration of a patient's stay can improve the efficiency of patient admissions.

In this project, we used both the MIMIC dataset and the Morbidities dataset to develop our predictive model. Techniques employed included decision trees, ensemble methods, and neural networks.