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Risk Prediction Model for Osteoporosis Disease Based on a Reduced Set of Factors
Abstract
The health industry collects huge amounts of health data, which, unfortunately, are not mined to discover hidden information. Information technologies can provide alternative approaches to the diagnosis of the osteoporosis disease. In this chapter, the authors examine the potential use of classification techniques on a huge volume of healthcare data, particularly in anticipation of patients who may have osteoporosis disease through a set of potential risk factors. An innovative solution approach based on dynamic reduced sets of risk factors using the promising Rough Set theory is proposed. An experimentation of several classification techniques have been performed leading to rank the suitable techniques. The reduction of potential risk factors contributes to enumerate dynamically optimal subsets of the potential risk factors of high interest leading to reduce the complexity of the classification problems. The performance of the model is analyzed and evaluated based on a set of benchmark techniques.
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