A1588
Title: Regression trees for analyzing longitudinal health data streams: A comparative study
Authors: Ines Sousa - Minho University (Portugal) [presenting]
Abstract: Chronic kidney disease (CKD) is characterized by kidney damage or an estimated glomerular filtration rate (eGFR) of less than 60 ml/min per 1.73 square meters for three months or more. The performance of six tree-based machine learning models - Decision Trees, Random Forests, Bagging, Boosting, Very Fast Decision Tree (VFDT), and Concept-adapting Very Fast Decision Tree (CVFDT)- are evaluated on longitudinal health data. Longitudinal data, where individuals are measured repeatedly over time, provide an opportunity to predict future trajectories using dynamic predictions that incorporate the entire historical dataset. These predictions are essential for real-time decision-making processes in healthcare. The dataset comprised 406 kidney transplant patients, spanning from January 21, 1983, to August 16, 2000. It captures 120 time points over the first 119 days post-transplant, including baseline glomerular filtration rates (GFR), along with three static variables: weight, age, and gender. Data preprocessing involved robust imputation techniques to handle missing data, ensuring consistency and trend accuracy. The models were trained to predict health outcomes starting from the eight-day post-transplant, progressively incorporating daily values to predict subsequent days up to day 119. Model performance was evaluated using mean squared error (MSE) and mean absolute error (MAE) through data partitioning and cross-validation techniques.
