Hybrid Neuro-Fuzzy Estimation of Cold- Urticaria Threshold and Severity from Multi- Sensor Provocative Testing

Danilychev et al. built TempPro, a 140 mm, 8-sensor applicator that measures the temperature threshold (TTV) of cold and heat urticaria, but it recovers the threshold with a fixed linear interpolation and reports no learning component or classification metrics. We ask whether soft computing can extract more from the same measurements. Because no public per-patient provocation dataset exists, we generate a simulated cohort of 2,000 sessions calibrated to the base paper's device physics and to published cohort statistics, and compare a first-order Takagi-Sugeno ANFIS (a neuro-fuzzy hybrid) with the paper's method, logistic/linear regression, a hand-written fuzzy inference system and an MLP. Over 5 by 3 repeated cross-validation, ANFIS raises severity ROC-AUC from 0.877 to 0.910, recall at 90 percent specificity from 0.677 to 0.773, and cuts threshold MAE from 0.752 to 0.465 degrees Celsius. It beats the MLP and the plain fuzzy system on most metrics while remaining a readable rule base. A genetic-algorithm-tuned ANFIS gives the same accuracy (no significant difference), and neither hybrid beats well-tuned logistic or linear regression. All results are from simulated data and show feasibility, not clinical validity.