This paper presents the research in developing an ensembleof data mining techniques for predicting the risk of osteoporosis prevalence in women. Osteoporosis is a bone disease that commonly occurs among postmenopausal women. Early detection and diagnosis is the key for prevention but are very difficult, without using costly diagnosing devices, due to complex factors involved and its gradual bone lose process with no obvious waning symptoms inparticular. Our research aims to develop an intelligent decision support system based on data mining ensemble technology to assist General Practitioners in assessing patient's risk of developing osteoporosis. The paper focuses on investigating the methodologies for constructing effective ensembles, specifically on the measurements of diversity between individual models induced by two types of machine learning techniques, i.e. neural networks and decision tress for predicting the risk ofosteoporosis. The constructed ensembles as well as their member predictors are assessed in terms of reliability, diversity and accuracy of prediction. The results indicate that the intelligently hybridised ensembles have high-level diversities and thus are able to improve their performance.
|Number of pages||106|
|Journal||WSEAS Transaction on Systems|
|Publication status||Published - 2005|