Abstract
The paper presents a new approach to effectively support the adaptation phases in the case-based reasoning (CBR) process. The use of the CBR approach in DSS (Decision Support Systems) can help the doctors better understand existing knowledge and make personalized decisions. CBR simulates human thinking by reusing previous solutions applied to past similar cases to solve new ones. The proposed method improves the most challenging part of the CBR process, the adaptation phase. It provides diagnostic suggestions together with explanations in the form of decision rules so that the physician can more easily decide on a new patient’s diagnosis. We experimentally tested and verified our semi-automatic adaptation method through medical data representing patients with cardiovascular disease. At first, the most appropriate diagnostics is presented to the doctor as the most relevant diagnostic paths, i.e., rules—extracted from a decision tree model. The generated rules are based on existing patient records available for the analysis. Next, the doctor can consider these results in two ways. If the selected diagnostic path entirely covers the actual new case, she can apply the proposed diagnostic path to diagnose the new case. Otherwise, our system automatically suggests the minimal rules’ modification alternatives to cover the new case. The doctor decides if the suggested modifications can be accepted or not.
Highlights
Machine learning promises significant advancement in the healthcare domain up to personalized medicine [1]
The case-based reasoning (CBR) method predicts future situations based on similar historical conditions. This approach itself is not entirely new, but after reading a number of studies dealing with case-based reasoning [2,3,4,5,6,7,8] and its applications in medical data [9,10,11,12,13,14,15,16,17,18,19,20,21], we found that this area still offers several research problems that have not yet received sufficient attention, or a suitable solution has not previously been proposed and tested [9]
Since we used the rule based casebased reasoning (CBR) system, we chose the decision trees method to generate the decision models as basis for the case base. We believe that this approach will increase the accuracy of cardiovascular disease’s diagnostic process, choose the right treatment method, or identify potentially useful and, at first glance, hidden knowledge
Summary
Machine learning promises significant advancement in the healthcare domain up to personalized medicine [1]. The most often used are predictive models, trained on various sorts of medical data. Predictions include the use of advanced analytical technology, modeling, machine learning, and data mining algorithms, running simulations, and pattern searching. Some machine learning models provide excellent predictive accuracy but do not offer understandable insights about their predictions. This is a severe problem in the medical domain. Explaining data patterns is useful in every way to understand and trust the system and its predictions. Data scientists interpret large amounts of data, use analytical algorithms, and convey knowledge through visual and textual descriptions to help people understand details
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