Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigatethe role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is acausative factor that can provide additional power for the diagnosisof CAN especially in the absence of a complete set of Ewing tests.We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes.AIME incorporate diverse ensembles into several tiers simultaneouslyand combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble.We carried out extensive experimental analysis using large datasets from the diabetes screening research initia(DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The resultsshow that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57%has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitionerscan use these findings to increase the accuracy of CAN diagnosis.