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

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 the R-squared coefficient (R²), root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE). The interpretability of the optimal model was investigated using SHapley Additive exPlanations (SHAP) to explain individual feature contributions. The hybrid PSO-CatBoost model provided better predictive accuracy (R2 = 0.9456; RMSE = 17.2891; MAE = 10.9230; MSE = 298.9114) as compared to other hybrid optimisation-model configurations. SHAP ranked right stride length and gait speed as the most important features. The hybrid PSO-CatBoost demonstrated high predictive accuracy in estimating functional capacity in patients with HF using gait parameters, but its potential as a clinical decision-support tool requires further validation.