A cost-sensitive k-nearest neighbor algorithm using non-invasive diagnostic tests predicted cardiovascular adverse events in women with suspected ischemic heart disease better than standard guidelines (HR 1.4, p=0.0002).
Does a cost-sensitive k-nearest neighbor algorithm improve the prediction of adverse cardiovascular events in women with suspected ischemic heart disease compared to conventional risk guidelines?
A cost-sensitive k-nearest neighbor algorithm using non-invasive diagnostic tests achieved an AUROC >0.70 for predicting adverse cardiovascular events in women with suspected ischemic heart disease, outperforming conventional AHA risk guidelines.
Effect estimate: HR 1.4
p-value: p=0.0002
We investigate machine learning methods for diagnostic screening of heart disease. Coronary heart disease is the leading cause of death in the US, causing more deaths than all types of cancers combined. Early diagnosis of heart disease in women is harder than it is in men and typically requires the administration of several clinical tests on the patient. Most risk stratification methods aggregate the results of such tests, including the risky, invasive procedures that cannot be administered on all patients. In this paper, our goal is to identify patients who are under high-risk of having heart disease and related adverse events, using a minimal number of diagnostic tests, especially less invasive ones. The low frequency of patients with severe heart disease in the dataset is challenging for most conventional machine learning methods. To overcome this problem, we develop and apply a cost-sensitive k nearest neighbor algorithm. Our contributions are two fold: First, we compare the predictive value of several diagnostic procedures for heart disease, including electrocardiography, angiography, radionuclide perfusion and conclude that in womens heart disease, certain combinations of noninvasive techniques are more predictive than some of the widely used invasive procedures. Then, we evaluate held out data and achieve an AUROC over 0.70, signifying valuable clinical utility, using only the least costly and least invasive tests.
Uguroglu et al. (Sun,) conducted a other in Ischemic heart disease (n=638). Cost-sensitive k-nearest neighbor (C-KNN) algorithm vs. American Heart Association (AHA) risk classification algorithm was evaluated on Cardiovascular adverse events (death, stroke, MI, or CHF) (HR 1.4, p=0.0002). A cost-sensitive k-nearest neighbor algorithm using non-invasive diagnostic tests predicted cardiovascular adverse events in women with suspected ischemic heart disease better than standard guidelines (HR 1.4, p=0.0002).
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