Mortality risk prediction using machine learning in heart failure patients using the mimic-iii clinical database

Abstract

Heart failure is a medical disorder characterized by mortality as one of its ultimate consequences. It is characterized by a multitude of signs and symptoms that often overlap with those observed in various other medical disorders and diseases. The collective manifestation of these signs and symptoms has the potential to result in the mortality of the patient. The capacity to anticipate the risk of mortality in individuals with heart failure enables healthcare professionals to allocate resources more effectively to mitigate or avert potential fatalities. A large dataset is recommended for creating machine learning models that can be accurately generalized. Medical data is generally difficult to obtain, and studies use data that is either collected firsthand or conduct secondary analyses on data that was collected by others. To this end, this study uses a curated dataset of a cohort of heart failure patients obtained from the MIMIC-III database, and the results were compared with previous studies that used a distinct, but much smaller dataset of heart failure patients. This has the added advantage of the research being reproducible. Within the context of the using machine learning to predict mortality risk in heart failure patients, this study examines many signs and symptoms associated with heart failure to ascertain the characteristics that can effectively forecast the risk of death. Additionally, the study aims to evaluate the extent to which these findings can be applied to heart failure patients as a whole. In the evaluation of serum creatinine, ejection fraction, and binned age as features, the experimental analysis findings provide evidence of the potential efficacy of machine learning in appropriately categorizing heart failure patients according to their risk of mortality. This information has the potential to enable doctors to take preemptive measures and enhance treatment procedures, ultimately resulting in enhanced patient outcomes and allocation of resources. This study investigates a range of machine learning techniques, including logistic regression, random forests, and gradient boosting, to determine the optimal method for predicting mortality risk. The research demonstrates the effectiveness of employing machine learning methods to leverage extensive clinical datasets such as MIMIC-III for the purpose of improving the accuracy of mortality risk prediction in patients with heart failure. The present study significantly contributes to the expanding field of predictive analytics in healthcare, providing vital insights for physicians and academics who seek to enhance heart failure therapy and improve patient care.