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020			_aTHE0010815 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKPM .M86 2026 r Thesis
100			_aSiti Munirah Muhammad Ali, _eauthor.
245	1	0	_aPatient-specific lumped parameter models for cardiovascular simulations / _cSiti Munirah Muhammad Ali
264		1	_aKuantan, Pahang : _bUMPSA, _c2026
264		4	_c© 2026
300			_axiv, 221 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-COM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Manufacturing and Mechatronic Engineering Technology
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2026
504			_aIncludes bibliographical references
520	3		_aMost 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.
610	2	0	_aFaculty of Manufacturing and Mechatronic Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aThesis _xDissertations
942			_2lcc _cTHESIS
999			_c105674 _d105680