Title

Rough-fuzzy Hybrid Approach for Identification of Bio-Markers and Classification on Alzheimer's Disease Data

Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Faculty of Computing, Health and Science

School

School of Computer and Security Science / Artificial Intelligence and Optimisation Research Centre

RAS ID

12682

Comments

This article was originally published as: Lee, C. , Lam, C. P., & Masek, M. (2011) Rough-fuzzy hybrid approach for identification of bio-markers and classification on Alzheimer's disease data. Paper presented at the IEEE International Conference on Bioinformatics and Bioengineering (BIBE). TaiChung, Taiwan. Original article available here

Abstract

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.