Rough-Fuzzy Hybrid Approach for Identification of Bio-markers and Classification on Alzheimer´s Disease Data

A new approach is proposed in this paper for identification of biomarkers and classification on Alzheimer´s disease data by employing a rough-fuzzy hybrid approach called ARFIS (a framework for Adaptive TS-type Rough-Fuzzy Inference Systems). In this approach, the entropy-based discretization technique is employed first on the training data to generate clusters for each attribute with respect to the output information. The rough set-based feature reduction method is then utilized to reduce the number of features in a decision table obtained using the cluster information. Another rough set-based approach is employed for the generation of decision rules. After the construction and the evaluation phases of the proposed rough-fuzzy hybrid system, the classification is carried out on the testing set of the given data. The experimental results showed that the proposed approach achieved compatible classification results on Alzheimer´s disease data compared to results from other existing approaches in the literature. It can be concluded that the proposed rough-fuzzy hybrid approach is a novel approach in predictive data mining in clinical medicine in terms of utilizing 1) rough set-based approaches for feature reduction and rule generation, 2) a hybrid fuzzy system for pattern classification, and revealing 3) rules for prediction of diagnostic results.