Rough set–BPSO model for predicting vitamin D deficiency in apparently healthy Kuwaiti women based on hair mineral analysis

Abstract

Vitamin D deficiency is prevalent in the Arabian Gulf region, especially among women. Recent researches show that, the vitamin D deficiency is associated with mineral status of patient. Therefore, it is important to assess the mineral status of patient to reveal the hidden mineral imbalance associated with vitamin D deficiency. A well-known test such as the red blood cells is fairly expensive, invasive, and less informative. On the other hand, a hair mineral analysis can be considered an accurate, excellent, highly informative tool to measure mineral imbalance associated with vitamin D deficiency. In this study, 120 apparently healthy Kuwaiti women were assessed for their mineral levels and vitamin D status by hair and serum samples, respectively. This information was used to build a computerized model that would predict vitamin D deficiency based on its association with the levels and ratios of minerals. The model introduces a two-stage reduction technique based on BPSO and rough set theory as attribute reduction and rules extraction to predicting vitamin D deficiency. The results show that the proposed model (RS + BPSO), not only can effectively detect the deficiency in vitamin D, but can also provide valuable information with regard to the mineral imbalance as a cause of deficiency which should be addressed in any treatment management. To the best of our knowledge, this is the first work that predicts vitamin D deficiency based on hair minerals analysis.