Rheumatoid Arthritis (RA) is a type of dreadful autoimmune disease that affects the entire human body, especially joints. Early diagnosis of RA is a challenging task for General Physicians, since the actual triggering mechanism is unpredictable. The capability of C4.5 was explored using the hybridization of Grey Wolf Optimization (GWO) - Particle Swarm Optimization (PSO) to develop an effective RA prediction system. In this work, firstly, PSO was applied for selecting the diversified initial positions. Secondly GWO was used to update the current positions of the population from the search space to get the optimal features for better classification. Subsequently, the selected features were given as an input to the C4.5 approach and developed a final RA predictor model. The proposed HGWO-C4.5 was meticulously examined based on real time patient’s data, which included factors that influence RA prediction by utilizing both RA and Non-RA information. To validate the proposed HGWO-C4.5, with other meta-heuristics based methods including GWO based C4.5, PSO based C4.5 and individual C4.5 method. The Cross-validation results show that HGWO-C4.5 has achieved an overall average accuracy of 86.36% from three other approaches, which was $\sim$6%–14% higher than those attainable using the individual predictors. Furthermore, HGWO-C4.5 has achieved an overall average accuracy of 84% on independent datasets evaluation, which was 8.61% higher than those yielded by the state-of-the-art predictors. This is the first predictor model that includes feature selection and classification for RA prediction to the best of our knowledge.

类风湿关节炎（RA）是一种可怕的自身免疫性疾病，会影响整个人体，尤其是关节。对RA的早期诊断对于普通医师而言是一项艰巨的任务，因为实际的触发机制是不可预测的。通过使用灰狼优化（GWO）-粒子群优化（PSO）的杂交方法探索C4.5的能力，以开发有效的RA预测系统。在这项工作中，首先，将PSO应用于选择多样化的初始职位。其次，使用GWO从搜索空间更新人口的当前位置，以获得最佳特征以进行更好的分类。随后，将选定的特征作为C4.5方法的输入，并开发了最终的RA预测器模型。拟议的HGWO-C4。根据实时患者数据仔细检查了图5，其中包括通过利用RA和Non-RA信息影响RA预测的因素。为了验证提出的HGWO-C4.5以及其他基于元启发法的方法，包括基于GWO的C4.5，基于PSO的C4.5和单独的C4.5方法。交叉验证结果表明，与其他三种方法相比，HGWO-C4.5的总体平均准确度为86.36％。$〜$比使用单个预测变量可达到的结果高6％–14％。此外，在独立的数据集评估中，HGWO-C4.5的总体平均准确度达到84％，比最新的预测指标高出8.61％。据我们所知，这是第一个包含用于RA预测的特征选择和分类的预测器模型。