<![CDATA[Asthma prediction demands architectures capable of capturing multifactorial interactions among demographic, clinical, and environmental determinants. This study establishes Random Forest (RF) as the optimal solution through rigorous comparison with Logistic Regression (LR) and Support Vector Machines (SVM) on a 10,000-patient cohort. RF achieved performance: 99.55% accuracy, 100% precision, 98.19% recall, and exceptional stability (σ=0.0019 CV) surpassing SVM by 6.86% recall, preventing 167 missed diagnoses per 10,000 cases. Hereditary factors dominated feature importance (Gini=0.20), generating 18.7% greater node purity reduction than BMI, while the paradoxical "No Allergies" signal (3.726) revealed non-atopic phenotypes. Critically, sparse linear correlations (94% |r|<0.02) contrasted with RF’s capture of nonlinear thresholds like sedentarism (2.243) > smoking impact. Clinical implementation requires: (1) threshold calibration (θ=0.3) achieving >99% recall, (2) monthly false-negative audits mitigating 24.33% prevalence skew, and (3) dimensionality reduction eliminating 3.256 features. RF’s capacity to resolve hereditary-environmental interactions establishes a new paradigm for asthma risk stratification.]]>
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