In a computational medical model, diagnosis is the classification of disease status in the terms of abnormal or positive, normal or negative or intermediate stages. Different Machine learning techniques such as artificial neural networks (ANNs) are extensively and successfully used in disease diagnosis. However, there is no single classifier that can solve all classification problems. Selecting an optimal classifier for a problem is difficult, and it has become a relevant subject in the area. This paper focuses on grammar-based automatic programming (GAP) to build optimized discriminant functions for medical data classification in any arbitrary language. These techniques have an implicit power of automatic feature selection and feature extraction. This work carries out an in-depth investigation of the use of different GAP algorithms in the medical data classification problem. The objective is to identify the benefits and limitations of algorithms of this nature in the current problem. Classical classifiers were also considered for comparison purposes. Fourteen medical data sets were used in the experiments, and seven performance measures such as accuracy, sensitivity, specificity, precision, geometric-mean, F-measure, and false-positive rate are used to evaluate the performance of the produced classifier. The multiple criteria decision analysis (MCDA) demonstrates that GAP approaches are able to produce suitable classifiers for a given problem, and the GS performs better than other classical classifiers in medical data classification.

在计算医学模型中，诊断是按照异常或阳性，正常或阴性或中间阶段来分类疾病状态。诸如人工神经网络（ANN）之类的不同机器学习技术已广泛且成功地用于疾病诊断。但是，没有单个分类器可以解决所有分类问题。为问题选择最佳分类器很困难，并且已成为该领域的相关主题。本文着重于基于语法的自动编程（GAP），以构建用于任意语言的医学数据分类的优化判别函数。这些技术具有自动特征选择和特征提取的隐含功能。这项工作对在医学数据分类问题中使用不同的GAP算法进行了深入研究。目的是确定这种算法在当前问题中的优点和局限性。还考虑将古典分类器用于比较目的。实验中使用了14个医学数据集，并使用了7个性能指标（如准确性，敏感性，特异性，精确度，几何均值，F指标和假阳性率）来评估所生成分类器的性能。多准则决策分析（MCDA）表明，GAP方法能够为给定问题生成合适的分类器，并且GS在医疗数据分类中的性能优于其他经典分类器。和假阳性率用于评估产生的分类器的性能。多准则决策分析（MCDA）表明，GAP方法能够为给定问题生成合适的分类器，并且GS在医疗数据分类中的性能优于其他经典分类器。和假阳性率用于评估产生的分类器的性能。多准则决策分析（MCDA）表明，GAP方法能够为给定问题生成合适的分类器，并且GS在医疗数据分类中的性能优于其他经典分类器。