Early diagnosis of gastrointestinal cancer by using case-based and rule-based reasoning

We present a medical diagnosis decision support model for gastrointestinal cancer.The model uses case-based reasoning (CBR) and rule-based reasoning (RBR).We used real patient data as inputs to our model.The model was validated through K-fold cross validation and the paired t-test.Improved diagnosis accuracy compared to a CBR approach not using RBR in retrieval. In this paper, we present a medical diagnosis decision support model for gastrointestinal cancer. It should be used by general practitioners whenever there is a suspicion that a patient has this type of cancer. To build our model, we used Case-Based Reasoning (CBR) and Rule-Based Reasoning (RBR). We used real patient data as inputs to our model. We applied RBR to improve the CBR retrieve process. The model's output presents the probability of the patient having a specific cancer. In order to adjust the attributes weights, we collected data from a general practitioner. To validate our model, we used K-fold cross validation and the paired t-test. The results showed that, with our approach, the accuracy of the diagnosis increased by 22.92% when compared to a CBR approach not using RBR in case retrieval. Furthermore, we evaluated our approach with an online questionnaire and semi-structured interviews. Even though, given the number of respondents, we cannot generalize our conclusions, the results indicate that our approach would be useful for general practitioners.

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