The use of Artificial Intelligence and Machine Learning algorithms in everyday life is common nowadays in several areas, bringing many possibilities and benefits to society. However, since there is room for learning algorithms to make decisions, the range of related ethical issues was also expanded. There are many complaints about Machine Learning applications that identify some kind of bias, disadvantaging or favoring some group, with the possibility of causing harm to a real person. The present work aims to shed light on the existence of biases, analyzing and comparing the behavior of different learning algorithms – namely Decision Tree, MLP, Naive Bayes, Random Forest, Logistic Regression and SVM – when being trained using biased data. We employed pre-processing algorithms for mitigating bias provided by IBM’s framework AI Fairness 360.

Performance analysis of machine learning algorithms trained on biased data

Image classification has been applied to several real problems.
However, getting labeled data is a costly task, since it demands time, resources and experts. Furthermore, some domains like disease detection suffer from unbalanced classes. These scenarios are challenging and degrade the performance of machine learning algorithms. In these cases, we can use \textit{Data Augmentation} (DA) approaches to increase the number of labeled examples in a dataset. The objective of this work is to analyze the use of Generative Adversarial Networks (GANs) as DA, which are capable of synthesizing artificial data from the original data, under an adversarial process of two neural networks. The GANs are applied in the classification of unbalanced Covid-19 radiological images. Increasing the number of images led to better accuracy for all the GANs tested, especially in the multi-label dataset, mitigating the bias for unbalanced classes.

Comparison of GANs for Covid-19 X-ray classification

The COVID-19 pandemic declared in March 2020 by the World Health Organization (WHO) challenged the health system of several countries with the growing number of infected people. During the pandemic's peak in Europe, the low incidence of infection in South Korea drew the international community's attention, since not long ago that country was considered the epicenter of the pandemic outside its origin, in China. The mass testing protocol and tracing policies were pointed out as the formula for South Korean success, however, in view of the high demand and little supply of diagnostic tests for COVID-19 in the market, this strategy proved to be unfeasible to be implemented mainly in countries with large populations and with few financial resources, such as Brazil. There is also the aggravating factor regarding the effectiveness of the tests currently available, especially the rapid serology test with a high rate of false negatives. In order to offer a screening method for the application of tests, this work aims to develop a predictive model for assisting the identification of COVID-19 infection in suspected patients based on data from clinical laboratory examinations, such as blood count and urine tests. The data used comes from three sources in Sao Paulo and are hosted in the COVID-19 Data Sharing/BR Repository, a shared database of Sao Paulo Research Foundation (FAPESP). The work also proposes a comparison between balanced x imbalanced dataset and traditional x ensemble algorithms for this problem.

Applying machine learning to assist the diagnosis of COVID-19 from blood and urine exams

**Chairs:** Elloa Barreto Guedes Da Costa,	Elaine Faria and Paulo T. Guerra 


Data Science

technical paper

**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). **ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

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BRACIS 2021

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (22 editions), and the Brazilian Symposium on Neural Networks – SBRN (13 editions). 
ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

Lilian Berton

3

Presentations

Performance analysis of machine learning algorithms trained on biased data

Comparison of GANs for Covid-19 X-ray classification

Applying machine learning to assist the diagnosis of COVID-19 from blood and urine exams

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