Application of Weighted Alternating Least Squares on Constructing the Disease Networks in the Heterogeneous Process of Aging

Nowadays, the number of comorbidities (physical-physical, mental-mental, physical-mental) is growing fast. The potential network structure of highly related diseases could be revealed and found by several approaches with the concept of disease network, such as Weighted Alternating Least Squares (WALS). The 2012 medical history of the Health Examination for the Elderly of Taipei City was used for this study. Based on traditional correlation analysis, the results show that physical and mental diseases/disorders have some special comorbid structure. Moreover, the correlations had some potential clusters with significant between-cluster separation. However, while we used WALS approach to explore the hidden structure of disease networks, the complex and unexpected disease networks of the aging population were revealed according to subjects' medical history. The hidden structure could be identified and further used for WALS calculating via matching controls who had no the specific disease to cases who carried it by other disease diagnosis. Our findings showed the predictive accuracy with 0.83 for the diagnostic model. It indicated the importance of hidden factors being used for further calculating the disease correlations of multisystem disorders.