Persian Medicine (PM) pulsology is a central diagnostic modality for assessing
an individual’s temperamental state and cardiac strength; however, its practical
application is inherently subjective and dependent on practitioner expertise. To
enhance objectivity and reproducibility in PM diagnostics, this study proposes
an artificial neural network (ANN) framework for the quantitative estimation of
four core temperamental qualities—warmness, coldness, wetness, and dryness—
together with cardiac strength, based solely on measurable pulse characteristics
Clinical data were collected from 69 individuals, comprising 11 pulse-derived features
and gender as inputs, with six corresponding diagnostic outputs. A multilayer
perceptron (MLP) architecture was trained and optimized using two learning
algorithms: Levenberg–Marquardt (LM) and Scaled Conjugate Gradient (SCG).
Comparative evaluation demonstrated the superiority of the SCG-trained network,
with an optimal configuration of 12 hidden neurons. This model achieved a test
accuracy of 96.43% and a low mean squared error (MSE) of 0.0139. Notably, cardiac
strength was predicted with 100% accuracy, while temperamental qualities
were classified with accuracies ranging from 71.43% to 92.86%. To enhance interpretability,
a sensitivity analysis was conducted, revealing pulse strength and pulse
frequency as the most influential predictors across multiple diagnostic outputs.
The proposed ANN-based system provides a stable and objective computational
surrogate for traditional PM pulsology. It offers practical utility for practitioner
training, supports diagnostic standardization, and establishes a methodological
foundation for future integrative and intelligent diagnostic platforms in Persian
Medicine.