Robust self management classification via sparse representation based discriminative model for mild cognitive impairment associated with diabetes mellitus

Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerably higher risk of dementia, whose daily self-care and life management (i.e. self-management) have a significant impact on the development of their condition. Thus, the inclusion and discrimination of subsequent interventions according to their self-management is an urgent issue. A Sparse-representation-based Discriminative Classification model (SDC) is proposed in this paper to correctly classify MCI-DM patients based on their self-management ability. Specifically, an L₁-minimization sparse representation model, an efficient machine learning model, is used to obtain the sparse histogram that encodes the identity of the test sample. Then, the coefficient of determination \(\:{R}^{2}\) is adopted to determine the category based on the sparse histogram of the test sample. Extensive experiments on the self-management data of DM-MCI are conducted to verify the effectiveness of SDC. The experimental results show that the accuracy \(\:\mathcal{A}\) , precision \(\:\mathcal{P}\) , recall \(\:\mathcal{R}\) , and F1-score \(\:\mathcal{F}\) are 94.3%, 95.0%, 94.3%, and 94.5%, respectively, demonstrating the excellent performance of SDC. The model used in this study has high accuracy and can be used for subgroup discrimination. The use of the sparse representation model in this study has supportive implications for the inclusion of research subjects in clinical intervention strategies.