Accuracy of neuro-fuzzy logic and regression calculations in determining maximal lactate steady-state power output from incremental tests in humans

The aim of this study was to employ neuro-fuzzy logic and regression calculations to determine the accuracy of prediction of the power output (P) of the maximal lactate steady-state (MLSS) on a cycle ergometer calculated from the results of incremental tests. A group of 17 male and 17 female sports students underwent two incremental tests (a 1 min test T₁: initial exercise intensity 0.2 W·kg^–1 increasing 0.2 W·kg^–1 every minute; a 3 min test T₃: initial exercise intensity 0.6 W·kg^–1 increasing 0.6 W·kg^–1 every 3 min) and at least four constant-intensity tests of 30 min duration. Two models for MLSS calculation were developed using the data from T₁ and T₃, a forward stepwise linear regression model (REG) and a neuro-fuzzy model (FUZ). A group of 26 randomly selected subjects (model group, MG) were used to generate the REG and the FUZ models. The data from the remaining 8 subjects (4 men and 4 women; verifying group, VG) were used to verify the REG and FUZ models. The precision of the MLSS calculation in MG produced a better correlation when using data from T₁ (REG r=0.95, FUZ r=0.99) than data from T₃ (REG r=0.88, FUZ r=0.98). Our calculation models were confirmed using data from VG for T₁ (REG r=0.97, FUZ r=0.98) as well as for T₃ (REG r=0.97, FUZ r=0.97). Based on our subject population of young, healthy sport students, our results suggest that a single incremental test may be used for prediction of P at the MLSS using a cycle ergometer. Furthermore, the results from T₁ yielded higher correlations compared to T₃. Calculations from REG were similar to FUZ but the precision of REG and FUZ was better compared to calculations derived using data from a single threshold. Electronic Publication