Machine Learning-Based Feature Selection and Classification for the Experimental Diagnosis of Trypanosoma cruzi
Machine Learning-Based Feature Selection and Classification for the Experimental Diagnosis of Trypanosoma cruzi
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DOI：
10.3390/electronics11050785
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发表时间：
2022-03-01
期刊：
影响因子：
2.9
通讯作者：
Haro, Paulina
中科院分区：
文献类型：
--
作者：
Hevia-Montiel, Nidiyare;Perez-Gonzalez, Jorge;Haro, Paulina
恰加斯病由克氏锥虫 (T. cruzi) 寄生虫引起，是全球第三大常见寄生虫病。如果没有及时、早期发现或未进行客观诊断，大多数感染者可能会保持无症状。通常，这种疾病会在很长一段时间后出现，并伴有严重的心脏病或突然死亡。因此，诊断是一个复杂且具有挑战性的过程，必须考虑多个因素。在本文中，提出了一种新颖的流程，集成了来自四种模式（心电图信号、超声心动图图像、多普勒频谱和 ELISA 抗体滴度）的时间数据、通过单变量分析和基于机器学习的选择进行的多特征选择分析。该方法包括基于随机森林、极度随机树、决策树和支持向量机的动物状态自动二分分类（对照与感染）。发现的最相关的多模式属性是 ELISA（IgGT、IgG1、IgG2a）、心电图（SR 平均值、QT 和 ST 间期）、升主动脉多普勒信号和超声心动图（舒张期左心室直径）。关于所选特征的自动分类，交叉验证中对照组与急性感染组的最佳准确度为 93.3 +/- 13.3%，最终测试为 100%；对照组与慢性感染组分别为 100% 和 100%。我们的结论是，所提出的基于机器学习的方法有助于在早期克氏锥虫感染阶段获得可靠且客观的诊断。
Chagas disease, caused by the Trypanosoma cruzi (T. cruzi) parasite, is the third most common parasitosis worldwide. Most of the infected subjects can remain asymptomatic without an opportune and early detection or an objective diagnostic is not conducted. Frequently, the disease manifests itself after a long time, accompanied by severe heart disease or by sudden death. Thus, the diagnosis is a complex and challenging process where several factors must be considered. In this paper, a novel pipeline is presented integrating temporal data from four modalities (electrocardiography signals, echocardiography images, Doppler spectrum, and ELISA antibody titers), multiple features selection analyses by a univariate analysis and a machine learning-based selection. The method includes an automatic dichotomous classification of animal status (control vs. infected) based on Random Forest, Extremely Randomized Trees, Decision Trees, and Support Vector Machine. The most relevant multimodal attributes found were ELISA (IgGT, IgG1, IgG2a), electrocardiography (SR mean, QT and ST intervals), ascending aorta Doppler signals, and echocardiography (left ventricle diameter during diastole). Concerning automatic classification from selected features, the best accuracy of control vs. acute infection groups was 93.3 +/- 13.3% for cross-validation and 100% in the final test; for control vs. chronic infection groups, it was 100% and 100%, respectively. We conclude that the proposed machine learning-based approach can be of help to obtain a robust and objective diagnosis in early T. cruzi infection stages.