Abstract

Cancer poses a significant challenge within the healthcare domain due to its complexity. Analyzing cancer treatment pathways is crucial to identify treatments that are more specific and personalized for patients, as well as to predict potential outcomes and associated costs. Consequently, it is essential to distinguish between various patient types and their respective treatments. Process mining techniques, which utilize data analytics to understand care processes based on event logs, can enhance our comprehension of treatment sequences within different groups. This paper aims to explore variations in treatment pathways for breast cancer, perform process discovery techniques, visualize models, assess quality through conformance checking, and enhance the process model. The proposed approach is implemented through a case study of a real-world dataset extracted from Indonesian Health Insurance records, where breast cancer diagnoses account for a significant portion (25.8%). This research investigates care pathways of breast cancer within three distinct treatment groups: radiotherapy, chemotherapy, and a combination of both. Notably, chemotherapy emerges as the most common treatment, and the majority of patients fall within the age range of 50’ to 59’. Furthermore, the sequence of treatments within the chemotherapy and radiotherapy groups exhibits a substantial divergence of 25%. The outcomes emphasize the applicability of the mining approach, illustrating the potential patterns of breast cancer treatment pathways.