Constructing binary decision trees for predicting Deep Venous Thrombosis

Deep Venous Thrombosis (DVT) is an intrinsic disease where blood clots form in a deep vein in the body. Since DVT has a high mortality rate, predicting it early is important. Decision trees are simple and practical prediction models but often suffer from excessive complexity and can even be incomprehensible. Here a genetic algorithm is used to construct decision trees of increased accuracy and efficiency compared to those constructed by the conventional ID3 or C4.5 decision tree building algorithms. Experimental results on two DVT datasets are presented and discussed.