Objective To perform a comparative evaluation of Mamdani and Takagi-Sugeno (TS) fuzzy inference systems for predicting peripheral vascular resistance from photoplethysmogram (PPG) signals, incorporating diagnostic parameters from Persian medicine (PM) pulsology.

Methods Both fuzzy inference systems were implemented in MATLAB R2021b and validated using leave-one-out cross-validation (LOOCV) on clinical PM pulse diagnostic data collected from 35 healthy volunteers. The dataset included pulse frequency scale (1 – 6) and weakness scale (1 – 4) alongside corresponding PPG-derived peripheral vascular resistance indices (range: 0.019 – 0.983). The Mamdani system (with 35 rules) was designed using trapezoidal and triangular membership functions, a singleton fuzzifier, a product inference engine, and a centroid defuzzifier. The TS system (with 6 rules) was configured as a first-order model with Gaussian input membership functions. System performance was quantitatively evaluated using mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R²). Statistical significance of performance differences was assessed using a paired t test.

Results The Mamdani inference system showed much higher prediction accuracy than the TS system. Comparative analysis revealed a substantial advantage for the Mamdani system (MAE = 0.007 63 ± 0.001 20, RMSE = 0.008 58 ± 0.001 50, R² = 0.998 40 ± 0.000 80) over the TS system (MAE = 0.015 40 ± 0.002 10, RMSE = 0.022 48 ± 0.002 80, R² = 0.989 20 ± 0.001 50). A paired t test comparing the absolute errors of the 35 LOOCV folds confirmed statistical significance t (34) = 5.07, P < 0.001.

Conclusion The findings suggest the potential suitability of the Mamdani system for certain precision-oriented analytical tasks. Both systems exhibit practical utility for integrative medicine diagnostics and wearable health-monitoring applications for healthy individuals, enabling a modern computational translation of PM pulsology.