Meta-heuristic and machine learning based functional capacity prediction using gait parameters in patients with heart failure.

ABSTRACT: This study aimed to predict functional capacity using wearable sensor-based spatiotemporal and kinematic gait parameters, and meta-heuristic-based machine learning approaches in patients with heart failure (HF). This cross-sectional study included 70 patients with HF and assessed functional capacity and gait parameters using the six-minute walk test and a wearable inertial sensor, respectively. The Synthetic Minority Over-Sampling Technique for Regression was employed to increase the number of instances. The optimisation of four machine learning models (XGBoost, LightGBM, Random Forest, CatBoost) was implemented using Simulated Annealing, Genetic Algorithms, Particle Swarm Optimisation (PSO) and Bayesian Optimisation. The model performance was evaluated via 10-fold cross-validation through

SUBMITTER: Tanriverdi Eyolcu A 

PROVIDER: S-EPMC13473135 | biostudies-literature | 2026 Jun

REPOSITORIES: biostudies-literature