Using data mining technology to predict medication-taking behaviour in women with breast cancer: A retrospective study

Aims: Medication-taking behaviours of breast cancer survivors undergoing adjuvant hormone therapy have received considerable attention. This study aimed to determine factors affecting medication-taking behaviours in people with breast cancer using data mining. Design: A longitudinal observational retrospective cohort study with a hospital-based survey. Methods: A total of 385 subjects were surveyed, analysing existing data from January 2010 to December 2017 in Taiwan. Three data mining approaches-multiple logistic regression, decision tree and artificial neural network-were used to build the prediction models and rank the importance of influencing factors. Accuracy, specificity and sensitivity were used as assessment indicators for the prediction models. Results: Multiple logistic regression was the most effective approach, achieving an accuracy of 96.37%, specificity of 96.75% and sensitivity of 96.12%. The duration of adjuvant hormone therapy discontinuation, duration of adjuvant hormone therapy use and age at diagnosis by data mining were the three most critical factors influencing the medication-taking behaviours of people with breast cancer.