A Random Forest based predictor for medical data classification using feature ranking 论文

摘要

Medical data classification is considered to be a challenging task in the field of medical informatics. Although many works have been reported in the literature, there is still scope for improvement. In this paper, a feature ranking based approach is developed and implemented for medical data classification. The features of a dataset are ranked using some suitable ranker algorithms, and subsequently the Random Forest classifier is applied only on highly ranked features to construct the predictor. We have conducted extensive experiments on 10 benchmark datasets and the results are promising. We present highly accurate predictors for 10 different diseases, as well as suggest a methodology that is sufficiently general and is expected to perform well for other diseases with similar datasets.