A prediction method of diabetes comorbidity based on non-negative latent features

In this paper, we present a novel network-based approach, namely Inherently Non-negative Latent Feature Analysis for Diabetes Mellitus Comorbidity Detection (INDM), to enhance the detection and analysis of comorbidities associated with diabetes mellitus. Different from existing methods, INDM is the first computational approach that integrates comorbidity networks of the chronic disease spectrum with patient clinical characteristics. To perform the analytical tasks, the proposed INDM adopts the following core components. First, comorbidity networks representing patients diagnosed solely with hypertension and those with hypertension and diabetes are constructed, following the case-control design that establishes a 1:1 matching in age and gender between two cohorts. Subsequently, the disease set is modeled in the comorbidity network according to the relative risk methodology. This enables nodes and edges in the comorbidity network to represent disease interactions that are derived from the patient-disease bipartite graph. Second, a nonlinear loss function with the capability of inherently non-negative latent feature analysis followed by a comorbidity classifier is adopted to uncover the patterns indicating the diabetes comorbidity in the comorbidity network. The proposed INDM has been rigorously tested on actual diabetes comorbidity datasets. The notable results demonstrate that INDM exhibits superior detection accuracy. Furthermore, the topological structure discovered by the proposed INDM can provide a profound insight into hypertension comorbidity in both the case and control groups.