Nonparametric detection scheme for myocardial infarction

Abstract

Bernstein et al.¹ have suggested a method for the detection of myocardial infarction using the combined measurement of serum LD activity and inhibition of LD by pyruvate (which depends on the amount of LD from damaged myocardium). This is another in the growing number of applications of discriminant analysis in medical diagnosis. As is often the case, the true underlying distribution of the data is not known. In this case, in particular, an attempt is made at defining the distribution to more accurately assess those patients among whom the diagnosis of myocardial infarction is suspect but is not clearly identified. Tsokos and Welch² have shown that discriminant procedures based on incorrect assumptions of the underlying distribution led to substantially higher error rates. In this paper, we consider the application of a nonparametric probability density estimator recently developed by David W. Scott.³ This leads to a rather accurate discriminant procedure that is applicable to many other types of data.