Unsupervised phenotypic clustering, inferential characterization, and discriminant classification of elite flatwater canoeists: neuromuscular and anthropometric determinants
Year: 2026
Type: article
Study design: Observational Study
Abstract: Introduction Phenotypic profiling of elite athletes remains constrained by supervised frameworks that conflate physiological characteristics with performance outcomes. Unsupervised machine learning offers an outcome-independent alternative for identifying intrinsic athlete profiles. Method This study applied clustering algorithms to comprehensive laboratory data from elite flatwater canoeists. Twenty-one international-level C1 canoeists (12 male, 9 female) underwent a standardized testing battery including body composition, muscle contractile properties, mobility, maximal and explosive strength, and sport-specific neuromuscular assessments via Functional Electromechanical Dynamometry (FEMD). All performance variables were excluded to ensure purely phenotypic segmentation. Preprocessing included asymmetric winsorization, Yeo-Johnson transformation, and robust scaling. K-Means and hierarchical clustering (Ward linkage) were applied with optimal k determined via silhouette coefficient, Davies-Bouldin index, and elbow method. Discriminant variable importance was assessed via Random Forest Classifier; interpretable classification rules were extracted using Decision Tree analysis (max_depth=3) with stratified train-test validation (66.7%–33.3%). Statistical characterization of cluster differences employed ANOVA or Kruskal-Wallis tests with post-hoc comparisons and Benjamini-Hochberg FDR correction; algorithmic concordance was quantified via Adjusted Rand Index (ARI). Results Three phenotypic clusters emerged: High-Neuromuscular Male Phenotype (n=7, 33.3%), Female-Dominant Phenotype (n=10, 47.6%), and Moderate-Neuromuscular Male Phenotype (n=4, 19.0%) (ARI = 0.591). Sex stratification was marked (χ²=18.90, p<0.001): the Female-Dominant Phenotype comprised 90% female athletes; both male phenotypes were exclusively male, encoding sexual dimorphism alongside intra-sex neuromuscular differentiation. FEMD neuromuscular variables dominated discrimination (75.9% of top-15 importance; 14 of 15 variables with p adj < 0.01 following Benjamini-Hochberg FDR correction). Decision Tree classification achieved 100% training accuracy and 71.4% test accuracy (5/7; 95% CI: 29.0%–96.3%; p=0.045 vs. chance), though the small test partition constrains generalizability. Within male phenotypes, Clusters 0 and 2 demonstrated equivalent neuromuscular profiles (all p > 0.05), with phenotypic separation captured by latissimus dorsi sustain time from the non-paddle side; differences relative to the Female-Dominant Phenotype primarily reflect sexual dimorphism. Discussion Unsupervised clustering suggests a tripartite phenotypic structure in elite canoeists consistent with sexual dimorphism and intra-sex neuromuscular differentiation, with FEMD-derived eccentric force as primary discriminant. Classification rules may inform future sex-stratified talent identification and training individualization; external validation with larger cohorts is required.
Source: Frontiers in Physiology
Institutions: Universidade da Coruña, Universidade de Vigo
Language: English
Subfield: Orthopedics and Sports Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7222933225