Global optimization of case-based reasoning for breast cytology diagnosis
ABSTRACT Case-based reasoning (CBR) is one of the most popular prediction techniques in medical domains because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation – its prediction performance is generally lower than other AI techniques like artificial neural networks (ANN). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GA). Our model improves the prediction performance in three ways – (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating useless or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.
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ABSTRACT: Emergency preparedness enables us to effectively handle sudden environmental events. In previous research, we have proposed to develop environmental emergency preparedness systems employing Case-Based Reasoning (CBR) technology, though developing such a CBR system has been stifled by a deficiency of cases and difficulties of case adaptation. In this paper, an Improved Genetic Algorithm (IGA) is put forward to resolve the issue of adaptability, and thus simultaneously overcoming the deficiency of cases.First we introduce the Frame method, which creates a case representation in accordance with the characteristics of, for instance, a sudden chemical leakage event and the system’s preparedness for treating this case. Then we present the principle of genetic algorithm (GA) for case adaptation. Next, we introduce an Improved Genetic Algorithm (IGA) that achieves case adaptation in the CBR system. The IGA overcomes simplex GA (SGA)’s defects including premature and slow convergence rate, and also enhances search efficiency for globally optimal solutions. The IGA employs technologies including the Multi-Factor Integrated Fitness Function, the Multi-Parameter Cascade Code method, the Small Section method for generation of an initial population, and Niche technology for genetic operations including selection, crossover, and mutation. The results of SGA and IGA performance testing are also presented. A prototype CBR-IGA environmental emergency preparedness system is developed and introduced, and its applicability is tested using a hypothetical ammonia leakage emergency in one district of Shanghai. The results indicate that the proposed IGA methodology can resolve the adaptation issue and expand the case base effectively in CBR systems for environmental emergency preparedness. Future research opportunities are discussed, including the potential to combine CBR, GA, and Back Propagation-Artificial Neural Network (BP-ANN) to alleviate additional challenges, such as the “speed and accuracy” of environmental emergency response.Expert Systems with Applications 06/2012; 39(8):7029–7040. · 1.85 Impact Factor
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ABSTRACT: Many CBR systems have been developed in the past. However, currently many CBR systems are facing a sustainability issue such as outdated cases and stagnant case growth. Some CBR systems have fallen into disuse due to the lack of new cases, case update, user participation and user engagement. To encourage the use of CBR systems and give users better experience, CBR system developers need to come up with new ways to add new features and values to the CBR systems. The author proposes a framework to use text mining and Web 2.0 technologies to improve and enhance CBR systems for providing better user experience. Two case studies were conducted to evaluate the usefulness of text mining techniques and Web 2.0 technologies for enhancing a large scale CBR system. The results suggest that text mining and Web 2.0 are promising ways to bring additional values to CBR and they should be incorporated into the CBR design and development process for the benefit of CBR users.Expert Systems with Applications 02/2013; 40(2):500–507. · 1.85 Impact Factor
- International Journal of Computer Applications. 06/2012; 47(7):14-21.