Immobility is an important health concern for the elderly patients and healthcare providers who care for the elderly. The purpose of this study was to test a knowledge discovery method to detect elderly patients with impaired mobility in a large clinical dataset. The research method applied an exploratory design and a data mining classification method (cost sensitive Decision Tree J48 from WEKA) to classify patients. Important factors were identified by the Feature Selection method. The Decision Tree algorithm classified patients in the dataset with 65% sensitivity and 72% specificity for a reduced model. The results were evaluated by 10-fold cross validation. Examples of decision rules were also extracted. The study can be applied to classify different health problems in different populations and serves as a foundation for the development of healthcare decision support systems.