Data-driven modeling approaches combined with physics-based cardiovascular simulations offer new opportunities for robust disease identification under limited and noisy clinical data. This work presents a hybrid framework for cerebral arterial occlusion localization based on discriminant analysis of Doppler-derived hemodynamic spectra. A labeled dataset of cerebral blood flow waveforms is generated using one-dimensional simulations of the cerebral arterial network, in which occlusion location is explicitly prescribed [1,2]. Doppler velocity signals obtained at the carotid arteries are analyzed in the frequency domain, and compact spectral representations are constructed using a systematic frequency-band selection strategy. Supervised classification is performed using Linear and Quadratic Discriminant Analysis, providing an interpretable and data-efficient alternative to more complex machine-learning models. To reflect realistic acquisition conditions, multiple sources of uncertainty affecting Doppler measurements are explicitly modeled, including additive electronic noise, signal-dependent scatterer noise, and impulsive motion artifacts. Classification performance is evaluated across a wide range of noise levels representative of clinical and prehospital scenarios. Results indicate that low-frequency spectral components (2–12 Hz) carry the most discriminative information for occlusion localization, with high classification accuracy maintained under significant noise contamination. Reliable localization is achieved using single-sided carotid measurements, suggesting that diagnostically relevant hemodynamic signatures propagate through the coupled cerebral arterial system. The proposed framework illustrates how physics-based cardiovascular modeling combined with lightweight data-driven inference can support robust diagnostic classification and forms a basis for future integration with patient-specific data and digital twin methodologies. REFERENCES [1] A. Sen, L. Navarro, S. Avril, M. Aguirre, “A data-driven computational methodology towards a pre-hospital acute ischaemic stroke screening tool using haemodynamics waveforms”, Computer Methods and Programs in Biomedicine 244 (2024) 107982. [2] J. Alastruey, K. Parker, J. Peiró, S. Byrd, S. Sherwin, “Modelling the circle of Willis to assess the effects of anatomical variations and occlusions on cerebral flows”, Journal of biomechanics 40 (8) (2007) 1794–1805.