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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- "Cancer Diagnosis (Glez-Pena, et al. 2009) Cancer Classification (Marling, Shubrook and Schwartz 2008) Diabetes (Ahn and Kim 2009) Breast Cancer Diagnosis (Obot and Uzoka 2009) Hepatitis (Ahmed, et al. 2009) "
ABSTRACT: This paper present the brief overview of different CDSSs proposed in order to facilitate the medical practitioners during diagnosis phase, with this we proposed an online KBCDSS. Using this system the medical practitioners of different medical domains gather over one platform from where they can check and verify the reliability of their decision making during diagnosis phase. Aim of the research is to provide guidance to the new medical practitioners as well as to experienced clinicians. Database management systems used as knowledge representation scheme and Case based reasoning technique is applied as an inference mechanism. This research performed data analysis on the Wisconsin breast cancer data set from UCI Machine Learning Repository and implements this medical data set on KBCDSS tool. Keywords: Clinical Decision Support Systems (CDSS), Knowledge based CDSS (KBCDSS), Knowledge Representation (KR), Database Management Systems (DBMS), Case-based reasoning (CBR), Breast Cancer.
- "In their study, vertical and horizontal dimensions of the research data are reduced through their proposed research model, the hybrid feature and instance selection process using genetic algorithms. Ahn and Kim  propose a novel approach to enhance the prediction performance of CBR by putting forward the suggestion of the simultaneous optimization of feature weights, instance selection , and the number of neighbors using genetic algorithms (GA). Sabum et al.  construct a radial basis function network (RBFN) composed of representative cases created by K-means clustering. "
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- "Two of the earliest medical CBR systems for diagnosis and decision support were CASEY for heart failure (Koton, 1989) and MEDIC for dyspnoea (Holt et al., 2006). More recent work in this area are systems for early detection of breast cancer (Hung & Chen, 2006), for diagnosing neuromuscular diseases (Pandey & Mishra, 2009a), diagnosing breast cytology (Ahn & Kim, 2009) and the system T-CARE, a temporal case retrieval system for medical scenarios used in an intensive care burn unit (Juarez et al., 2011). CBR applications in health care are mostly used for determining diagnoses and as a medium for treatment (Pandey & Mishra, 2009b). "
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; 32(2). DOI:10.1111/exsy.12074 · 0.75 Impact Factor