Discovering Risk Factors of High Intraocular Pressure Infection Using Data Mining Algorithms

Eye diseases are very common diseases in the medical field. High intraocular pressure is one of serious eye diseases, which leads to the loss of vision in the affected eye. Thus, how to effectively discover the hidden risk factors from large data of high intraocular pressure is a very significant and challenging issue. Data mining algorithms (such as classification and association rules) can discover the hidden knowledge from large-scaled data sets in many real-world applications especially in the medical field. Therefore, this paper focuses on finding out the risk factors hidden in high intraocular pressure effectively and efficiently by using J48, and Apriori algorithms. In terms of these data mining algorithms, we can discover some interesting and useful rules in the data set of eye disease. Results of the study discover some risk factors of high intraocular pressure which have been proved by medical trials. The experimental study demonstrates the effective and usability of data mining techniques in Medical field. Meanwhile, this study discovers some new risk factors for high intraocular pressure which have not been discovered previously by studies based on medical trials. This knowledge will contribute to early detection and preventing blindness by recognizing risk factors for high intraocular pressure.