In recent days, the gigantic generation of medical data from smart healthcare applications requires the development of big data classification methodologies. Medical data classification can be utilized for visualizing the hidden patterns and finding the presence of disease from the medical data. In this article, we present an efficient multi‐kernel support vector machine (MKSVM) and fruit fly optimization algorithm (FFOA) for disease classification. Initially, FFOA is employed to choose the finest features from the available set of features. The selected features from the medical dataset are processed and provided to the MKSVM for medical data classification purposes. The proposed chronic kidney disease (CKD) classification method has been simulated in MATLAB. Next, testing of the dataset takes place using the own benchmark CKD dataset from UCI machine learning repositories such as Kidney chronic, Cleveland, Hungarian, and Switzerland. The performance of the proposed CKD classification method is elected by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, and false negative rate. The investigational outcome specifies that the proposed CKD classification method achieves maximum classification precision value of 98.5% for chronic kidney dataset, 90.42904% for Cleveland, 89.11565% for Hungarian, and 86.17886% for Switzerland dataset than existing hybrid kernel SVM, fuzzy min‐max GSO neural network, and SVM methods.

中文翻译：

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利用多核支持向量机和果蝇优化算法对慢性肾脏病进行有效分类

近年来，从智能医疗应用程序中生成巨大的医疗数据需要开发大数据分类方法。医学数据分类可用于可视化隐藏模式并从医学数据中发现疾病的存在。在本文中，我们提出了一种用于疾病分类的高效多核支持向量机（MKSVM）和果蝇优化算法（FFOA）。最初，使用FFOA从可用的功能集中选择最佳功能。处理从医学数据集中选择的特征，并将其提供给MKSVM以进行医学数据分类。所提出的慢性肾脏疾病（CKD）分类方法已在MATLAB中进行了仿真。下一个，使用UCI机器学习存储库（例如肾脏慢性病，克利夫兰，匈牙利和瑞士）自己的基准CKD数据集对数据集进行测试。CKD分类方法的性能由准确性，敏感性，特异性，阳性预测值，阴性预测值，假阳性率和假阴性率来选择。研究结果表明，与现有的混合核SVM，模糊最小-最大GSO相比，拟议的CKD分类方法对慢性肾脏数据集的最大分类精度值达到98.5％，对克利夫兰而言为90.42904％，对于匈牙利人来说为89.11565％，对于瑞士数据集为86.17886％。神经网络和SVM方法。所提出的CKD分类方法的性能由准确性，敏感性，特异性，阳性预测值，阴性预测值，假阳性率和假阴性率来选择。研究结果表明，与现有的混合核SVM，模糊最小-最大GSO相比，拟议的CKD分类方法对慢性肾脏数据集的最大分类精度值达到98.5％，对克利夫兰而言为90.42904％，对于匈牙利人来说为89.11565％，对于瑞士数据集为86.17886％。神经网络和SVM方法。CKD分类方法的性能由准确性，敏感性，特异性，阳性预测值，阴性预测值，假阳性率和假阴性率来选择。研究结果表明，与现有的混合核SVM，模糊最小-最大GSO相比，拟议的CKD分类方法对慢性肾脏数据集的最大分类精度值达到98.5％，对克利夫兰而言为90.42904％，对于匈牙利人来说为89.11565％，对于瑞士数据集为86.17886％。神经网络和SVM方法。