Predicting Deep Venous Thrombosis Using Binary Decision Trees

An intrinsic disease where blood clots form in a deep vein in the body is known as Deep Venous Thrombosis (DVT). 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.