COMPARING VARIOUS MACHINE LEARNING METHODS FOR PREDICTION OF PATIENT REVISIT INTENTION: A CASE STUDY

Many techniques have been proposed for analysis of costumer intention, from surveys to statistical models. During the last few years, different machine learning approaches have successfully been applied to costumer-centric decision-making problems. In this study, we conduct a comparative assessment of the performance of ten widely used machine learning methods, (i.e., logistic regression, multilayer perceptron, support vector machines,  IBk linear NN search, KStar, locally weighted learning, decisionstump, C4.5., randomtree and  reduced error pruning tree) for the aim of suggesting appropriate machine learning techniques in the context of patient revisit intention prediction problem. Experimental results reveal that the C4.5 decision tree demonstrates to be the best predictive model since it has the highest overall average accuracy and a very low percentage error on both Type I and Type II errors, closely followed by the locally weighted learning and decisionstump, whereas the logistic regression and the IBk linear NN search algorithms appear to be the worst in terms of average accuracy and type II error. Besides the randomtree and the IBk linear NN search algorithms appear to be the worst in terms of type I error