Nearly 90% of older Canadians have at least one chronic disease; 65% have two or more. The aims of my thesis were to apply business analytics techniques to predict the presence of an exemplar chronic disease, heart disease, among older Canadians, and to calculate the corresponding expected healthcare costs. I used neural networks to develop logistic regression models of heart disease using demographic, lifestyle, and health information for 15,599 older adults from the Canadian Longitudinal Study on Aging. The Economic Burden of Illness in Canada provided healthcare cost data. The best model identified 65.8% of heart disease cases from 40% of participants with the highest predicted probabilities of heart disease, accounting for $2.7 million more expected annual healthcare costs than a randomly sampled 40%. Among all older Canadians, this difference would be $1.1 billion. These methods could assist healthcare decision makers to optimize the delivery of chronic disease prevention interventions.
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Thesis advisor: Mackey, Dawn
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