Extraction of Association Rules from Cancer Patient’s Records using F-P Growth Algorithm

Abstract

Cancer is a leading cause of mortality worldwide, and Sudan has a high cancer burden. The issue is that the data acquired from cancer patients grows yearly, and standard methodologies for analyzing this data are no longer adequate. Data mining techniques such as frequent pattern analysis and association rule mining are utilized in this research to assist in identifying hidden patterns and relationships in data. These strategies were utilized to provide valuable insights into the spread of cancer in Sudan and to assist healthcare professionals in making better diagnosis and treatment decisions. Support and confidence were utilized as measurement criteria. Support is used to evaluate the frequency of occurrence of an item or set of items among all transactions. In contrast, confidence is used to assess the strength of the relationship between groups of things. According to the findings, women are more likely than men to be diagnosed with cancer. The most common cancers in both genders include breast, prostate, ovarian, esophagus, and cervical cancers.

Keywords

• cancer
• association rules
• f-p growth
• confidence
• data mining