Date of Award
May 2021
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Bioengineering
Committee Member
William J Richardson
Committee Member
Jordon A Gilmore
Committee Member
Marc R Birtwistle
Abstract
Chronic pressure overload (PO) due to arterial hypertension can lead to structural changes within the heart including left ventricular hypertrophy (LVH) and eventually diastolic heart failure (DHF). The initial diagnosis of PO and LVH is typically challenging and costly, and thus, a new predictive diagnostic tool is desired. In a recent paper by Zile et al., it was found, through the use of a simple multivariate logistic regression, that there exists a multi-biomarker panel with predictive capabilities for the classification of patients with LVH and DHF. In our new work, we furthered the investigation into the plasma biomarker panel proposed by Zile et al. and have shown a proof-of-concept for predictive capabilities of select biomarkers through the use of machine learning classification strategies. An optimized ensemble boosting classification algorithm5 showed greater promise for the diagnosis of LVH and DHF within this population of heart patients. In select simulations, AdaBoost accurately categorized 79.2% of patients with LVH and 93.0% of patients with DHF with the inclusion of demographic, plasma biomarkers, and select echocardiogram data, and 77.2% and 91.5%, respectively, of patients with only select demographic and blood plasma panel data. Although these classification algorithms show promise as a diagnostic tool, we believe that further investigation into the specific biomechanical interactions involved in structural alterations of cardiac tissue through collagen turnover is warranted. To better connect the remodeling-related biomarkers to LVH and DHF prediction, we also constructed an ODE-based mechanistic model of type I collagen and employed a genetic algorithm to determine kinetic parameters for the system through DHF patient classification.
Recommended Citation
Ward, Michael William, "Diagnosis of Myocardial Hypertrophic Disease States Through Machine Learning and Mechanistic Modeling" (2021). All Theses. 3484.
https://tigerprints.clemson.edu/all_theses/3484