Abstract
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.
Highlights
The clinical diagnosis of a disease is a complex process since there are several factors and symptoms that must be taken into account and analyzed and integrated by clinical expertise
The peak of parasites was detected at day 30 (0–2,450,000 parasites/mL) and no parasites were detected on peripheral blood during days 60, 90 and 120; the mortality rate was over 50.8%
The parasitemia curve was similar to the results reported in other studies using the same strain of T. cruzi [28–30]
Summary
The clinical diagnosis of a disease is a complex process since there are several factors and symptoms that must be taken into account and analyzed and integrated by clinical expertise. Machine learning offers medical personnel with techniques and methods that allow the selection of appropriate variables, a rational classification of patients based on their stage for a given illness, and an accurate prediction of disease progression. These computational methods and algorithms can help to obtain a robust and objective clinical diagnosis. For some clinical diagnoses, more complex situations can occur, such as diseases with long asymptomatic periods, as is the case for Chagas disease (CD) patients. CD is endemic in 21 countries in Latin America and, due to migration, cases in Canada, United States of America, European, African, Eastern Mediterranean and Western
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