Preliminary diagnosis of medical conditions, such as autism spectrum disorder (ASD), requires an understanding of the influential traits, for example autistic traits, during the screening process. Therefore, selecting the right attributes is a critical part of model construction in medical applications such as ASD screening, as this directly impacts the accuracy and efficiency of classification. This research investigates different methods for selecting attributes, including Chi-square (CHI), correlation feature set, information gain, Gini index and fast correlated-based filter to identify highly impactful autistic traits using over 1000 data observations of cases and controls related to toddlers. We seek to find the common autistic traits that influence the pre-diagnosis process for ASD obtained by these attribute selection methods from a real autism dataset related to toddlers and their impact on the performance of the screening process. To achieve the aim, an empirical methodology involving the use of three classification algorithms, AdaBoost, k-Nearest Neighbour (kNN) and ID3, has been used to derive models from the various different datasets chosen prior to training according to the considered attribute selection methods. These models are evaluated using evaluation metrics including specificity, sensitivity, accuracy and area under curve. Empirical results using the classification techniques for different attribute sets for the toddlers’ dataset show which influential autistic traits can be utilized by clinicians and diagnosticians to speed up the pre-diagnosis process for ASD and to enhance classification performance. More importantly, we show which attribute selection methods identify the relevant attributes that influence the preliminary process for diagnosis.

对诸如自闭症谱系障碍（ASD）之类的医学疾病的初步诊断，需要在筛选过程中了解影响性状，例如自闭症性状。因此，在ASD筛选等医学应用中，选择正确的属性是模型构建的关键部分，因为这直接影响分类的准确性和效率。这项研究调查了多种选择属性的方法，包括卡方（CHI），相关特征集，信息增益，Gini指数和基于快速相关的过滤器，以使用1000多个与幼儿相关的病例和对照的数据观察来识别具有高度影响力的自闭症特征。我们试图从与幼儿相关的真实自闭症数据集中找到影响通过这些属性选择方法获得的ASD的预诊断过程的常见自闭性特征，以及它们对筛查过程性能的影响。为了实现这一目标，我们采用了一种经验方法，其中涉及使用三种分类算法AdaBoost，k-最近邻（kNN）和ID3已用于根据考虑的属性选择方法从训练之前选择的各种不同数据集中得出模型。这些模型使用评估指标进行评估，包括特异性，敏感性，准确性和曲线下面积。使用针对幼儿数据集的不同属性集的分类技术的经验结果表明，临床医生和诊断医生可以利用哪些有影响的自闭症特征来加快ASD的预诊断过程并提高分类性能。更重要的是，我们展示了哪种属性选择方法可以识别影响初步诊断过程的相关属性。