Machine Learning for Asthma Management: An Integrated Framework Combining Environmental Prediction of Respiratory Hospitalizations with Digital Biomarkers of Adherence to Diaphragmatic Breathing

(1) Background: Asthma is a chronic respiratory disease shaped by environmental, meteorological, and behavioral factors. Although the literature has advanced in predicting respiratory outcomes and in deploying digital technologies for therapeutic support, few approaches integrate population surveillance and individual monitoring within the same analytical framework. (2) Methods: This work developed an integrated machine learning framework composed of two complementary studies. Study 1 modeled the daily count of hospital admissions for all respiratory diseases (chapter X of the ICD-10, which includes asthma, COPD, and respiratory tract infections), denoted HOSPCIDX, in the municipality of São Paulo (Brazil). It drew on six years of data (2017 to 2022) from SIH/SUS via PCDaS/Fiocruz, CETESB, and INMET, and applied a hybrid architecture combining ElasticNet, residual CatBoost, direct CatBoost, and adaptive blending, validated through walk-forward over 30 bimonthly folds across the 2018 to 2022 period (2017 was reserved for lag construction). Study 2 focused specifically on pediatric asthma and analyzed 913 qualified records from the Respire Bem system, collected from patients aged 6 to 16 years, using XGBoost and Random Forest models. The clinical outcomes (Asthma Control Test and salivary cortisol) were generated by evidence-based synthetic simulation. (3) Results: In Study 1, the hybrid model achieved a mean MAE of 18.22, a mean RMSE of 23.99, a mean R² of 0.675, and a mean skill gain of 41.5% over the seasonal baseline. SHAP analysis identified mean temperature, PM2.5, NO2, and CO as the main predictive drivers of respiratory hospitalizations. In Study 2, XGBoost reached an R² of 0.80 for the simulated Asthma Control Test and 0.78 for simulated salivary cortisol, with self-reported sentiment emerging as the leading digital biomarker. (4) Conclusions: The proposed framework demonstrates the feasibility of a dual analytical architecture for asthma management, combining environmental prediction at the population level with digital monitoring at the individual level. Study 1 provides robust predictive validation with real data, while Study 2 represents an exploratory stage based on real behavioral data and simulated clinical outcomes, which calls for prospective validation with direct clinical and biological measurements.