Classification of thalassemia data using random forest algorithm

Abstract

Abstract Thalassemia is a blood disorder that occurred in Southeast Asia. Thalassemia cannot be cured, but early detected thalassemia with screening process is the best way to prevent thalassemia disease. If early detection is done, patients can get the right treatment. It helps them increase their life expectancy and reduce the risk of thalassemia to the next generation. In this paper, we use thalassemia data and propose a random forest method to classify thalassemia disease well and accurately. The result concludes that the random forest algorithm can give the best accuracy, precision and recall which is 100 percent by using multiple five in range of 70 to 85 percent as the training data.