Mortality Prediction in ICU Patients Using Machine Learning Models

Effective utilization of limited intensive care unit (ICU) allocations is a challenging task for medical experts to save precious human lives. The prolonged ICU stay of relatively secure patients and patients with low chances of recovery can cause life-threatening effects on patients waiting for ICU accommodation. Machine learning-based techniques for early prediction of mortality can help in this regard. This paper presents two mortality prediction models using the support vector machine (SVM) and linear discriminant analysis. The proposed models use clinical data of the ICU patients for early prediction of mortality. Distribution filtering is performed using the chi-square distribution during pre-processing. A subset of the publicly available Medical Information Mart for Intensive Care (MIMIC-III v1.4) dataset is used for the evaluation of the proposed models. The problem of class imbalance is handled by synthetic minority oversampling technique. The comparison of obtained results is performed with existing SVM and multiple logistic regression models to show the effectiveness of the proposed models. The results of the study can be helpful for clinical experts for better decision making regarding the utilization of ICU allocations.