M.S. Applied Data Science - Capstone Chronicles 2025

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Figure 2 Pearson correlation matrix of dietary intake and lifestyle variables. Strong correlations were observed between caloric intake and both fat and sugar, indicating overlapping dietary patterns that may influence model interpretation.

Beyond dietary intake patterns, broader lifestyle and behavioral indicators were examined to determine whether they showed stronger associations with metabolic syndrome status. One such measure, the lifestyle effort score, is a composite index intended to reflect the extent to which individuals engage in health-promoting behaviors. For this analysis, the score was divided into three categories—“Poor,” “Moderate,” and “Good”—using equal-width bins based on score ranges (0–1, 1–2, and 2+),

allowing for clearer comparisons across lifestyle levels. Figure 3 examines the association between these lifestyle categories and metabolic syndrome status. Across all three groups, the proportion of individuals with metabolic syndrome remained remarkably consistent at roughly 60%. This uniformity suggests that, within this dataset, higher lifestyle effort scores did not correspond to a lower prevalence of metabolic syndrome. Several possible explanations could account for

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