Exploring Co-occurrence Patterns in Infectious Diseases using Association Rule Mining: A Hospital-based Study from the Cities of Nepal - Kafle, S. C.; Nain, M.; Sharma, B.; Adhikari, H. P.; Raman, R. (Journal of Neonatal Surgery, 2025)
The co-occurrence of patient characteristics with infectious diseases provides essential insights into disease distribution, supporting improved preparedness and response strategies in both endemic and epidemic contexts. While Association Rule Mining (ARM) is widely applied in computer science for pattern discovery, its use in healthcare, particularly for understanding demographic and geographic clustering of infectious diseases, remains limited. This study applied the Apriori algorithm, a commonly used ARM technique, to retrospective inpatient data from three tertiary hospitals in Nepal—Kathmandu, Bharatpur, and Pokhara—totaling 1,019 records. Rules were evaluated based on support, confidence, and lift to identify strong, non-redundant associations. The findings showed that Dengue consistently clustered among younger urban populations, with gender and caste (particularly males and Brahmins) emerging as key relationships, and showed distinct geographical concentrations in Kathmandu and Pokhara. In contrast, Scrub typhus was strongly associated with older adults, females, and rural residents, with exceptionally high lift values observed in Chitwan, indicating a substantially elevated risk compared to the general inpatient population. No significant rules emerged for Kala-azar due to its low case frequency. These findings underscore the heterogeneity of vector-borne disease distribution across ecological and demographic contexts. The study concludes that ARM is a valuable analytic tool for uncovering non-random, epidemiologically meaningful patterns, offering practical guidance for targeted interventions, resource allocation, and the development of context-specific public health strategies in Nepal and similar regions