Multi-Objective PSO-fuzzy Optimization Approach to Improve Interpretability and Accuracy in Medical Data
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2018-07-06 
https://doi.org/10.14419/ijet.v7i4.31.29943
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Fuzzy Rule-based Classification, Multi-objective PSO, Particle Swarm Optimization (PSO), Rule-based Systems, Single-objective PSO -
Today, Decision Support Systems (DSS) plays a significant role in a medical and healthcare domain. Designing an Automatic Fuzzy
Rule-based Classification Systems (FRBCSs) is considered as optimization problem associated to a result of high interpretability and
accuracy. Interpretability and accuracy are the two main objectives to be improved in the optimization measurement of FRBCSs. However, improving these objectives is found to be difficult in most of the existing systems due to the conflicting issues between accuracy
and interpretability. In this work, we proposed an approach that can effectively handle accuracy- interpretability trade-off in constructing
FRBCSs. We designed automated FRBCSs in the form of Multi-objective Particle Swarm Optimization with Crowding Distance. In the
approach, there will be a collection of solutions to FRBCSs that deem best global minimum or global maximum with respect to interpretability and accuracy. Our method is evaluated on a popular benchmark data sets being used in a medical domain for evaluations. These
datasets are Liver Disorders (BUPA), Pima Indians Diabetes and Thyroid Disease (New Thyroid). The result obtained shows that the
proposed method yields an optimum solution in minimizing the trade-off between accuracy and interpretability. Moreover, the result of
the comparison shows that our approach outperforms the alternate techniques in terms of accuracy of FRBCSs and also exhibits good
result in terms of interpretability objective.
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- [1] N. Esfandiari, M. R. Babavalian, A. M. E. Moghadam, and V. K.
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How to Cite
Batoul Rashed A, A., Hamdan, H., Sulaiman, M. N., Sharef, N. M., & Yaakob, R. (2018). Multi-Objective PSO-fuzzy Optimization Approach to Improve Interpretability and Accuracy in Medical Data. International Journal of Engineering & Technology, 7(4.31), 316-321. https://doi.org/10.14419/ijet.v7i4.31.29943
