Siti Munirah Muhammad Ali creator author. text my 2026 2026 monographic eng xiv, 221 pages : illustrations (some color) ; 30 cm. + 1 CD-COM Most lumped parameter (LP) cardiovascular models currently rely on population-averaged parameters, as practical methodologies to estimate personalized parameter values from routinely available non-invasive data remain limited. This study addresses this methodological gap by developing a patient-specific framework that combines an LP cardiovascular model with a genetic algorithm (GA) to estimate individualized physiological parameters. The proposed framework reconstructs individual hemodynamic states using only standard clinical inputs, including heart rate and systolic and diastolic blood pressure. Influential model parameters were identified through sensitivity analysis and subsequently optimized via a genetic algorithm, using clinical blood pressures as calibration targets and cardiac index as a soft constraint to ensure physiological validity. Validation on clinical data from 40 patients demonstrated that the model accurately replicates patient-specific hemodynamic and distinguishes between normotensive and Stage 2 hypertensive profiles without requiring prior diagnostic labels. Statistical analysis confirmed significant differences in estimated parameters, such as systemic arterial resistance (Rsysar) and compliance (Csysar), aligning closely with clinical expectations. This research demonstrates a robust, data-efficient approach for personalizing cardiovascular simulations using universally assessable measurements, bridging the divide between mechanistic modelling and practical clinical applicability. specialized Siti Munirah Muhammad Ali Faculty of Manufacturing and Mechatronic Engineering Technology Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2026 Includes bibliographical references Faculty of Manufacturing and Mechatronic Engineering Technology Dissertations Universities and colleges Dissertations Thesis Dissertations THE0010815 (Local) UMPSA 261006 20261006105515.0 eng