This paper presents a case study of using data mining techniques in the analysis of diagnosis and treatment events related to Breast Cancer disease. Data from over 16,000 patients has been pre-processed and several data mining techniques have been implemented by using Weka Waikato Environment for Knowledge Analysis. In particular, Generalized Sequential Patterns mining has been used to discover frequent patterns from disease event sequence profiles based on groups of living and deceased patients. Furthermore, five models have been evaluated in Classification with the objective to classify the patients based on selected attributes. This research showcases the data mining process and techniques to transform large amounts of patient data into useful information and potentially valuable patterns to help understand cancer outcomes.
['Jing Lu', 'Alan Hales', 'David Rew', 'Malcolm Keech', 'Christian Fröhlingsdorf', 'Alex Mills-Mullett', 'Christian Wette']
Data Mining Techniques in Health Informatics: A Case Study from Breast Cancer Research