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.
- SourceAvailable from: Salama MostafaInternational Journal of Computer Applications. 06/2012; 47(7):14-21.
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ABSTRACT: Case-based reasoning systems for medical application are increasingly multi-purpose systems and also multi-modal, using a variety of different methods and techniques to meet the challenges from the medical domain. In this paper, some of the recent medical case-based reasoning systems are classified according to their functionality and development properties. It shows how a particular multi-purpose and multi-modal case-based reasoning system solves these challenges. For this a medical case-based reasoning system in the domain of psychophysiology is used.Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases; 02/2009
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ABSTRACT: For patients with mental health problems, various treatments exist. Before a treatment is assigned to a patient, a team of clinicians must decide which of the available treatments has the best chance of succeeding. This is a difficult decision to make, as the effectiveness of a treatment might depend on various factors, such as the patient's diagnosis, background and social environment. Which factors are the predictors for successful treatment is mostly unknown. In this article, we present a case-based reasoning approach for predicting the effect of treatments for patients with anxiety disorders. We investigated which techniques are suitable for implementing such a system to achieve a high level of accuracy. For our evaluation, we used data from a professional mental healthcare centre. Our application correctly predicted the success factor of 65% of the cases, which is significantly higher than the prediction of the baseline of 55%. Under the condition that the prediction was based on only cases with a similarity of at least 0.62, the success rate of 80% of the cases was predicted correctly. These results warrant further development of the system.Expert Systems 03/2014; · 0.77 Impact Factor