Wearable sweat sensing enables non-invasive screening and monitoring, but concentration-only readouts are confounded by secretion dynamics such as instantaneous rate and accumulated volume and by evaporation. These limitations are especially problematic in clinically important low-sweat regimes, including mild or treatment-modulated states, observed in hyperhidrosis. We present a dual-mode, rate-aware platform that fuses resistive wetting and contact conductance with a capacitive absorbent-dielectric channel to infer local sweat volume and rate, supported by a skin-interfaced microfluidic layer for chrono-sampling and evaporation compensation. On this hardware, we introduce RAISE, Rate-Aware Inference with Sensor Ensembles, an uncertainty-informed pipeline that derives rate-normalized features, applies probability calibration using slope and intercept with expected calibration error and Brier score, and quantifies clinical utility with decision-curve analysis, while reporting uncertainty using bootstrap confidence intervals. Using patient-level splits and an external temporally held-out validation cohort, our approach improved discrimination and reliability over a concentration-only baseline, with delta AUC of 0.06 and external AUC of 0.92 with 95 percent confidence interval from 0.88 to 0.95. It reduced expected calibration error to 0.031 and Brier score to 0.121, and yielded higher net benefit across clinically relevant thresholds, with maximum delta of 0.06 and up to 7 avoided interventions per 100 at probability threshold 0.10. Sensor characterization achieved mean absolute percentage error below 10 percent versus gravimetry with R squared up to 0.992 across four infusion rates. By coupling dual-mode sensing with rate-aware calibration and decision-focused analytics, the system delivers clear, clinically interpretable gains for dependable hyperhidrosis screening and monitoring.