Association Rule Mining for Healthcare Data Analysis

In the domain of healthcare, massive amounts of data both unstructured and structured are created. As a result, a significant amount of money and time are required for storing and analysing it. The health industry has seen significant changes in recent years, with a growth in the number of physicians, patients, diseases, and technology. Doctors can analyse patient symptoms using data and information technology. Data mining is widely used for analysing these data. Association rule mining is one of the most significant tasks in data mining. Techniques such as apriori and FP-growth may be used to analyse data for illness diagnosis. The prime objective is to uncover the hidden associations between symptoms as well as statistically confirming those that are already known. These connections can aid in a better knowledge of illnesses and their causes, which will aid in their prevention. Association rules connect different diseases and treatments, as well as provide important information to doctors and health institutions in society. These rules are useful in healthcare research and development in areas such as potential complications, preventive medicine, disease diagnosis, and prevention. This chapter provides the complete information about association rule mining algorithms used in the healthcare domain. It also analyses critically existing association rules, relationships among various diseases, and discovers strong association rules from the healthcare data.