Latent Class Analysis

How it works

Comparisons

Evidence quality

Highlighted claims

• Latent Class Analysis was used as an unsupervised feature selection method that did not use cancer diagnosis as an outcome variable. — Exploring a panel of serum biomarkers for cancer risk in patients with non-specific symptoms: a comparative analysis of feature selection methods
• The LCA analysis required complete data and therefore used a 1,545-patient subset with complete biomarker records. — Exploring a panel of serum biomarkers for cancer risk in patients with non-specific symptoms: a comparative analysis of feature selection methods
• The LCA model grouped patients into latent classes based on categorical biomarker values defined by clinical laboratory cut-offs. — Exploring a panel of serum biomarkers for cancer risk in patients with non-specific symptoms: a comparative analysis of feature selection methods
• The LCA score had higher sensitivity than the LASSO score but lower specificity. — Exploring a panel of serum biomarkers for cancer risk in patients with non-specific symptoms: a comparative analysis of feature selection methods
• A three-class LCA solution was selected based on AIC, BIC, and chi-square statistics. — Exploring a panel of serum biomarkers for cancer risk in patients with non-specific symptoms: a comparative analysis of feature selection methods
• LCA's broader biomarker inclusion reflected its unsupervised design and captured overall illness severity rather than cancer alone. — Exploring a panel of serum biomarkers for cancer risk in patients with non-specific symptoms: a comparative analysis of feature selection methods