Topological Risk-Landscape in Metric-Free Categorical Database

Topological Risk-Landscape in Metric-Free Categorical Database

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The Entropy-based Categorical Exploratory Data Analysis (CEDA) paradigm is elaborately refined to algorithmically explore the intricate high-order directional associative relational patterns within the heterogeneous chronical disease dynamics captured by Behavioral Risk Factor Surveillance System (BRFSS) database.Operating on this imbalanced categorical dataset represented fully by its metric-free high-dimensional histogram, our algorithms conduct data-driven computations to investigate chronic disease mechanisms dozy dinkum moppet across four sub-populations along the age-axis, culminating in comprehensive systemic understandings.Upon this categorical data-world, CEDA first recognizes the category-oriented 1D histogram as the simplest form of a piece of explainable information.Then, utilizing Kolmogorov’s randomness-proper-based reliability check, CEDA identifies and confirms collectives of 1D histograms as major feature-categories of varying orders within each sub-population.These confirmed i want choo 60ml major feature-categories’ binary memberships are then arranged into a subject-vs-feature-category bipartite network heatmap, revealing serial horizontal and vertical blocks framed by clusters of similar subjects characterized by individual-risk-landscapes (IRL) against clusters of structurally dependent major feature-categories.

Based on such block-series, sub-population-specific disease mechanisms emerge as collective high-order interacting effects, elucidating directional associative relationships from study subjects’ topological neighborhoods to response-categories.Notably, the topological individual-risk-landscape offers profound insights into complex system dynamics and simultaneously exposes atypical subjects as explainable errors across all Machine Learning classifiers.