A Comprehensive Study of Predictive Models For Mortality Risk Assessment in Medical Data

This research paper presents an in-depth investigation into the performance of machine learning, ensemble techniques, and deep learning models for mortality risk assessment using Electronic Health Record (EHR) patient data. The study aims to offer a comprehensive understanding of the strengths and limitations of various approaches in predicting mortality risk, contributing to the advancement of data-driven healthcare applications. This study explores a diverse set of algorithms, ranging from traditional machine learning models to ensemble methods and deep learning architectures, evaluating their efficacy in classifying patients into different mortality risk categories. The experiments involve a large-scale analysis of real-world EHR datasets, encompassing a wide range of patient attributes and medical histories specific to mortality risk assessment. The results provide nuanced insights into the comparative performance of these models, shedding light on their abilities to handle the complexity and heterogeneity of healthcare data in the context of mortality risk. The findings, rooted in the implementation of 16 models for comparative analysis and to identify the best predictive model, have the potential to inform the development of more effective predictive models for mortality risk assessment. Ultimately, this research contributes to improved patient outcomes and the development of evidence-based medical decision support systems.