Like many medical diagnoses, clinical decision support system (CDSS) is essential to diagnose the cardiovascular autonomic neuropathy (CAN). However, diagnosis of CAN using the traditional ‘Ewing battery test’ becomes very difficult due to the inherent imbalanced and incompleteness condition in the collected clinical data. This influences the health professionals to investigate other related diagnostic reports of patients, including Electrocardiogram (ECG) data from ECG sensors, blood chemistry, podiatry and endocrinology features. However, additional components increase the dimensionality of the feature set as well as its heterogeneity and modality in the clinical data which may limit the applications of traditional data mining approaches for an accurate diagnosis of CAN in the CDSS. To address the aforementioned problem, in this paper, we have proposed, a novel multistage fusion approach based on a generative model and a statistical process control (SPC) technique to diagnose CAN more accurately. The proposed approach develops two different generative models by using a shared and a separated Independent Component Analysis (ICA) to overcome the incompleteness and modality of the data. Due to the heterogeneous and non-normality features, statistical correlations and multivariate control limits in relation to the CAN diagnosis parameters are determined by fusioning of a series of exponentially weighted moving average (MEWMA) control processes. Fusioned features from both component analyses and SPC are applied in an ensemble classification system. The proposed multistage fusion approach is experimentally verified to justify its performance by using a large dataset collected from the diabetes screening research initiative (DiScRi) project at Charles Sturt University, NSW, Australia. Our comprehensive experimental results show that the proposed fusion approach performs better than the standard classifier for both ‘Ewing’ feature set and ‘Ewing and additional feature set’ with significant improvement in accuracy.