Abstract: Illness diagnosis is the essential step in designating a treatment. Nowadays, Technological advancements in medical equipment can produce many features to describe breast cancer disease with more comprehensive and discriminant data. Based on the patient’s medical data, several data-driven models are proposed for breast cancer diagnosis using learning techniques such as naive Bayes, neural networks, and SVM. However, the models generated are hardly understandable, so doctors cannot interpret them. This work aims to study breast cancer diagnosis using the associative classification technique. It generates interpretable diagnosis models. In this work, an associative classification approach for breast cancer diagnosis based on the Discrete Equilibrium Optimization Algorithm (DEOA) named Discrete Equilibrium Optimization Algorithm for Associative Classification (DEOA-AC) is proposed. DEOA-AC aims to generate accurate and interpretable diagnosis rules directly from datasets. Firstly, all features in the dataset that contains continuous values are discretized. Secondly, for each class, a new dataset is created from the original dataset and contains only the chosen class’s instances. Finally, the new proposed DEOA is called for each new dataset to generate an optimal rule set. The DEOA-AC approach is evaluated on five well-known and recently used breast cancer datasets and compared with two recently proposed and three classical breast cancer diagnosis algorithms. The comparison results show that the proposed approach can generate more accurate and interpretable diagnosis models for breast cancer than other algorithms.
Keywords: Breast cancer diagnosis, interpretable diagnosis, associative classification, population-based intelligence, discrete equilibrium optimization algorithm
