Machine Learning-Enhanced Survival Analysis: Identifying Significant Predictors of Mortality in Heart Failure

State of the art machine learning methods can enhance the analysis of clinical data and improve the ability to predict patient outcomes because data collected from clinical records, such as heart failure mortality studies, are often high dimensional, heterogeneous and give challenges to traditional statistical analysis techniques. To address this challenge, this study conducted a survival analysis based on a dataset of 299 patients with heart failure, using Python libraries. Cox regression was used to model and analyse mortality, and to find which features are strongly associated with this outcome. The Kaplan-Meier survival curve approach was used to show the patterns of patient survival over time. The analysis showed that age, ejection fraction, and serum creatinine level were significantly (p <= 0.001) associated with mortality. Anaemia and creatinine phosphokinase also reached statistical significance (p-values 0.026 and 0.007, respectively). The Cox model showed good concordance (0.77) with the data, suggesting that the identified variables are useful for predicting mortality in patients with heart failure.