Osteoporosis Identification Using Data Mining Techniques

Abstract

Data Mining is a technique for discovering information results from large databases. A large database represents a huge amount of information that can be potentially very useful if discovered and summarized correctly. This paper presents a research in developing data mining ensembles for predicting the risk of osteoporosis prevalence in human. Osteoporosis is a bone disease that commonly occurs among postmenopausal women and no effective treatments are available at the moment, except prevention, which requires early diagnosis. However, early detection of the disease is very difficult. This research aims to devise an intelligent diagnosis support system by using data mining ensemble technology to assist General Practitioners assessing patient’s risk at developing osteoporosis. This paper describes the methods for constructing effective ensembles through measuring diversity between individual predictors. Apriori-PT are implemented by neural networks training. The ensembles built for predicting osteoporosis are evaluated by the real-world data and the results indicate that the algorithm has relatively high-level of diversity and thus are able to improve prediction accuracy