Using data mining technology to predict medication‐taking behaviour in women with breast cancer: A retrospective study - Chen‐Chen Kuo; Hsiu‐Hung Wang; Li‐Ping Tseng (Wiley, 2022)
Abstract 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.