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A discrete equilibrium optimization algorithm for breast cancer diagnosis

INTELLIGENT DATA ANALYSIS 2023年第4期27卷 1185-1204页

作者： Haouassi, Hichem Mahdaoui, Rafik Chouhal, Ouahiba Univ Khenchela Fac ST ICOSI Lab Khenchela Algeria

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.

关键词： Breast cancer diagnosis interpretable diagnosis associative classification population-based intelligence discrete equilibrium optimization algorithm

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An efficient classification rule generation for coronary artery disease diagnosis using a novel discrete equilibrium optimizer algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期43卷 2315-2331页

作者： Haouassi, Hichem Mahdaoui, Rafik Chouhal, Ouahiba Bekhouche, Abdelaali Univ Abbas Laghrour Dept Math & Comp Sci ICOSI Lab Khenchela Algeria

Many machine learning-based methods have been widely applied to Coronary Artery Disease (CAD) and are achieving high accuracy. However, they are black-box methods that are unable to explain the reasons behind the diagnosis. The trade-off between accuracy and interpretability of diagnosis models is important, especially for human disease. This work aims to propose an approach for generating rule-based models for CAD diagnosis. The classification rule generation is modeled as combinatorial optimization problem and it can be solved by means of metaheuristic algorithms. Swarm intelligence algorithms like equilibrium Optimizer algorithm (EOA) have demonstrated great performance in solving different optimization problems. Our present study comes up with a Novel discrete equilibrium Optimizer algorithm (NDEOA) for the classification rule generation from training CAD dataset. The proposed NDEOA is a discrete version of EOA, which use a discrete encoding of a particle for representing a classification rule;new discrete operators are also defined for the particle's position update equation to adapt real operators to discrete space. To evaluate the proposed approach, the real world Z-Alizadeh Sani dataset has been employed. The proposed approach generate a diagnosis model composed of 17 rules, among them, five rules for the class "Normal" and 12 rules for the class "CAD". In comparison to nine black-box and eight white-box state-of-the-art approaches, the results show that the generated diagnosis model by the proposed approach is more accurate and more interpretable than all white-box models and are competitive to the black-box models. It achieved an overall accuracy, sensitivity and specificity of 93.54%, 80% and 100% respectively;which show that, the proposed approach can be successfully utilized to generate efficient rule-based CAD diagnosis models.

关键词： Coronary artery disease medical diagnosis machine learning rule-based diagnosis rule discovery population-based optimization discrete equilibrium optimization algorithm

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