Diabetes mellitus is one of the most important public health problems affecting millions of people worldwide. An early and accurate diagnosis of diabetes mellitus has critical importance for the medical treatments of patients. In this study, first, artificial neural network (ANN) and classification and regression tree (CART)‐based approaches are proposed for the diagnosis of diabetes. Hybrid ANN‐GA and CART‐GA approaches are then developed using a genetic algorithm (GA) to improve the classification accuracy of these approaches. Finally, the performances of the developed approaches are evaluated with a Pima Indian diabetes data set. Experimental results show that the developed hybrid CART‐GA approach outperforms the ANN, CART, and ANN‐GA approaches in terms of classification accuracy, and this approach provides an efficient methodology for diagnosis of diabetes mellitus.

中文翻译：

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使用人工神经网络和遗传算法优化的分类和回归树诊断糖尿病

糖尿病是影响全球数百万人的最重要的公共卫生问题之一。糖尿病的早期准确诊断对于患者的医学治疗至关重要。在这项研究中，首先，提出了基于人工神经网络 (ANN) 和分类与回归树 (CART) 的方法来诊断糖尿病。然后使用遗传算法 (GA) 开发混合 ANN-GA 和 CART-GA 方法，以提高这些方法的分类精度。最后，使用 Pima Indian 糖尿病数据集评估所开发方法的性能。实验结果表明，开发的混合 CART-GA 方法在分类精度方面优于 ANN、CART 和 ANN-GA 方法，