Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in medical data classification studies. LR, a statistical fitting model, is suggested in medical problems because of its well-established methodology and coefficients contributing to the evaluation of clinical interpretations. ANNs are graphical models structured with node networks interconnected with arcs each of which is expressed in terms of weights discovered throughout the modeling process. Since ANNs have a complex structure with its layers and nodes in the layers, which provides ANNs the ability to model any data with complex relationships. Among the various models having origins in statistics and computer science, LR and ANNs have prevailed in the area of mass medical data classification. In this study, we introduce the 2 aforementioned approaches in order to generate a model dichotomizing 75 opioid-dependent patients and 59 control subjects from each other. Quantitative electroencephalography (QEEG) absolute power value of each electrode were calculated for 4 consecutive frequency bands namely delta, theta, alpha, and beta with the frequencies, 0.5 to 4, 4 to 8, 8 to 12, and 12 to 20 Hz, respectively. Significant independent variables contributing to the classification were underlined in LR while a feature selection (FS) method, genetic algorithm, is being applied to the ANN model to reveal more informative features. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores, and Gini coefficient. Although ANN-based classifier outperformed compared with LR, both models performed satisfactorily for absolute power measure in beta frequency band. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies.

中文翻译：

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二项逻辑回归和人工神经网络方法使用定量 EEG 功率测量对依赖阿片类药物的受试者和对照组进行分类

逻辑回归 (LR) 和人工神经网络 (ANN) 是医学数据分类研究中广泛提及的方法。LR 是一种统计拟合模型，在医学问题中被推荐使用，因为其完善的方法和系数有助于评估临床解释。人工神经网络是由节点网络构成的图形模型，节点网络与弧互连，每个弧都用在整个建模过程中发现的权重表示。由于 ANN 具有复杂的结构，其层和层中的节点为 ANN 提供了对具有复杂关系的任何数据进行建模的能力。在起源于统计学和计算机科学的各种模型中，LR 和 ANN 在海量医学数据分类领域占主导地位。在这项研究中，我们引入了上述 2 种方法，以生成一个模型，将 75 名阿片类药物依赖患者和 59 名对照受试者分开。计算每个电极的定量脑电图 (QEEG) 绝对功率值，分别针对 delta、theta、alpha 和 beta 4 个连续频段，频率分别为 0.5 至 4、4 至 8、8 至 12 和 12 至 20 Hz . LR 中强调了对分类有贡献的重要自变量，同时将特征选择 (FS) 方法、遗传算法应用于 ANN 模型以揭示更多信息特征。最后比较分类器的性能，考虑整体分类准确度、接受者操作特征曲线下的面积和基尼系数。尽管与 LR 相比，基于 ANN 的分类器的表现优于 LR，但两种模型在 beta 频段的绝对功率测量方面都表现令人满意。我们的结果强调了引入的方法的潜在好处是有希望的，并且将被视为二分物质使用障碍受试者和其他医学数据分析研究的临床接口。