Distributed Gradient Boosting Machines (DGBM) with Feature Selection via Particle Swarm Optimization for Predicting Disease Progression

This research embarked on a journey to explore the integration of Particle Swarm Optimization (PSO) with Distributed Gradient Boosting Machines (DGBM) for the precise prediction of disease progression using the MIMIC-III dataset. Amidst the burgeoning volume of healthcare data, the study aimed to pinpoint salient features and employ robust predictive modeling for enhanced accuracy. The combined PSO-DGBM model showcased a remarkable harmony between PSO's heuristic optimization capabilities and DGBM's distributed ensemble learning power. Preliminary results exhibited marked improvements in true positive rates and precision-recall metrics. Furthermore, the refined feature selection process revealed crucial indicators pivotal to understanding disease trajectories. The significance of this research extends beyond academic realms; it presents a data-driven tool that stands to revolutionize clinical scenarios. In essence, this research offers a novel approach, blending advanced algorithms with rich datasets, and underscores its potential in reshaping predictive healthcare strategies.