On Data Classification of Cataract Patients for Selecting Intraocular Lens Power Formula

In this paper, we present a method of selecting intraocular lens (IOL) power formulas for cataract patients. The method is based on support vector machines (SVM´s) and genetic algorithm (GA). We assume that each of patients´ data belongs to one of the classes named by three power formulas. We therefore consider the formula selection to be the issue of classifying patients´ data. Since three formulas have variables associated with axial length and corneal refractive power, we always employ them as elements in data. If there are elements probably useful in classifying the data, they are found by GA. We construct the final discrimination model by SVM learning using data with the above elements. The model consists of three coordinate spaces, each of which has two regions corresponding to two formulas. A space provides a potential solution. We finally take a majority of the potential solutions, and determine the formula suitable to some patient specified by it. We show that the proposed method achieves the most favorable percentage of concordance for the classification, if the two-point crossover technique is adopted in GA.