A highly connected world, in which physical distances among countries have been virtually reduced by modern ways of transportation, like airplanes, is also a more fragile world, from an epidemiological point of view. The ways by which the international commerce flows are the same ways used by vectors of infec-5 tious diseases. SARS and MERS are diseases disseminated by the coronaviruses SARS-Cov and MERS-Cov, respectively. They were responsible for critical outbreaks in 2002 and 2012, in this order, transmitting diseases whose main symptoms were respiratory. In December 2019, started perhaps the most critical outbreak of the recent hundred years: the rapid widespread of the coron-(MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-Cov) can cause acute myocarditis and heart failure (Zheng et al., 2020) . According to Zheng et al. (2020) some of the coronaviruses impacts in cardiovascular system 45 are the increase in blood pressure, and increase in troponin I (hs-cTnI) levels (Zheng et al., 2020) . Besides that patients with Covid-19 can developed lymphopenia (low level of lymphocytes in the blood) (Fan et al., 2020; Tan et al., 2020; Liu et al., 2020a) , leukopenia (few white blood cells). They can also have decrease in their hemoglobin levels, Absolute Lymphocyte Count (ALC) 50 and Absolute Monocyte Count (AMC) (Fan et al., 2020) . Patients that developed severe forms of the disease have significantly higher levels of hematological characteristics as Interleukin-6, D-dimer than patients that developed moderate form of the Covid-19 (Gao et al., 2020) . Therefore, considering that Covid-19 is a disease that affects blood parameters, hematological tests can be used to 55 diagnosis the disease.
Machine learning, a branch of the Artificial Intelligence in Computer Science, is the area that studies techniques specialized in pattern detection. Machine learning techniques have been used to many pattern detection tasks as image classification (Lerner et al., 1994; Phung et al., 2005; Barbosa et al., 2020b;  60 Gomes et al., 2020b) , image reconstruction (Gomes et al., 2020a) , biosignals analyzing (Müller et al., 2008; Karlik, 2014; Jambukia et al., 2015; Andrade et al., 2020) , hematological parameters analyzing (Tanner et al., 2008; Luo et al., 2016; Gunčar et al., 2018) and etc.
In this work, we propose a web diagnosis support system of the Covid-19 65 based in machine learning techniques. This system uses blood tests to diagnose Covid-19. Our machine learning method were training using the database provided by Hospital Israelita Albert Einstein located in São Paulo, Brazil. The database is formed by information from 5644 patients among them 559 patients were diagnosis with Covid-19 by RT-PCR with DNA sequencing and identifi-70 cation and additional laboratory tests during a visit to the hospital (Kaggle, 2020) . For each patient the database have more than one hundred laboratory tests like blood counts and urine test. From this database we set a new one that 4 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020 . . https://doi.org/10.1101 contains only 41 blood tests recommended by the Brazilian Ministry of Health when dealing with Covid-19 patients. Our goal is to provide a web tool that per-75 form accurate Covid-19 diagnosis with friendly interface and low computational cost.
Several studies have shown evidence of the relationship between and the blood. Moreover, they emphasize the importance of blood tests for 80 the diagnosis process of this disease. There are also studies that point to the relevance of using hematological analysis as an indicative of the severity degree of Covid-19. Fan et al. (2020) analyzed hematological parameters of 69 patients with Covid-19. The study was conducted with subjects from the National Center for Infectious Diseases (NCID) in Singapore. 65 of these patients underwent 85 complete blood count (CBC) on the day of admission. 13.4% of patients needed intensive care unit (ICU) care, especially the elderly. During the first exams, 19 patients had leukopenia (few white blood cells) and 24 had lymphopenia (low level of lymphocytes in the blood), with 5 cases classified as severe (Absolute Lymphocyte Count (ALC) < 0.5 × 10 9 /L). The study also pointed out that 90 patients who needed to be admitted to the ICU had lower ALC and a higher rate of Lactate Dehydrogenase (LDH). These data indicated that monitoring these parameters can help to identify patients who need assistance in the ICU. The authors found that the patients who were in the ICU had a significant decrease in their hemoglobin levels, ALC and Absolute Monocyte Count (AMC) levels, 95 when compared to the non-ICU group. ICU patients also tend to neuthophilia.
The platelet count did not prove to be a factor for discrimination between the type of hospitalization.
The work from Tan et al. (2020) also assessed the complete blood count of patients. They used data from both cured patients and patients who died from case of patients who died, blood tests were continuously monitored throughout the treatment process. Similar to the previous study, the authors observed lymphopenia in this group. Based on this, the study then outlined a model 105 (Time-LYM% model, TLM) for classifying disease severity and predicting prognosis. Thus, the blood lymphocyte percentage (LYM%) was divided into two cases, considering the first 10-12 days of symptoms: LYM% > 20% are classified as moderate cases and with a high chance of recovery. LYM% < 20% are classified as severe cases. In a second exam, 17-18 days after the first symptoms, 110 patients with LYM% > 20% are recovering, patients with 5 <LYM% < 20% are in danger, and LYM% < 5% are in critical condition. In order to validate the model, the authors evaluated 90 patients with Covid-19. The consistency between Guideline and TLM-based disease classification was verified using kappa statistic (Kappa = 0.48). These results indicate that probably LYM% should 115 be used together with other parameters for a better evaluation of Covid-19.
In Gao et al. (2020) (PCT); and Interleukin-6 (IL-6). Using statistical tests, the study noted that 130 the levels of GLU, CRP, IL-6, TT, FIB, and d-D were significantly higher in the severe group than in the mild group. Performing this analysis with ROC curves, the authors pointed out that the best indicators for predicting severity 6 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 16, 2020 . . https://doi.org/10.1101 were IL-6 and d-D combined, with AUC of 0.840. The combination also achieved specificity of 96.4% and sensitivity of 93.3%, using tandem and parallel testing, 135 respectively. These results indicate that patients with severe conditions would have abnormal coagulation. Liu et al. (2020a) reported that lymphopenia and inflammatory cytokine storm are abnormalities commonly found in other infections caused by coronavirus, such as SARS-Cov and MERS-Cov. With that in mind, they studied 40 140 patients diagnosed with Covid-19 confirmed by throat-swab specimens analyzed with RT-PCR. The patients were treated at Wuhan Union Hospital in January, 2020. The information provided was: epidemiological, demographic, clinical manifestations and laboratory tests. Similar to the previous study, patients were divided into two groups: mild patients, with symptoms such as epidemi-145 ological history, fever or respiratory symptoms, and abnormalities in imaging tests; the second group with severe patients, patients should additionally have symptoms such as shortness of breath, oxygen saturation < 93%, respiratory > 30 times/min, or PaO 2 /FiO 2 < 300 mmHg. 27 patients were classified in the first group, while 13 were classified in the second. The study reported 150 that levels of fibrinogen, D-dimer, total bilirubin, aspartate transaminase, alanine transaminase, lactate dehydrogenase, creatine kinase, C-reactive protein (CRP), ferritin and serum amyloid A protein were significantly higher in the severe group. Futhermore, most severe patients presented lymphopenia, that can be related to the significantly decreased absolute counts of T cells, especially 155 CD8+ T cells, while white blood cells and neutrophils counts were higher.
These studies have pointed out that hematological parameters can be indicators of the risk factors and degree of severity of Covid-19. The identification of these parameters can be essential to optimize clinical care for each group of patients. In this sense, the development of intelligent systems based on blood 160 tests is useful. Faced with the pandemic scenario, in which most hospitals are full, decision support systems can facilitate clinical management. Thus, it can increase the assertiveness in the treatment for each case and, consequently, the number of lives saved.
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. reached an accuracy of 57% considering all the diseases chosen. By restricting the prediction to five classes, the systems achieved an accuracy of 88% and 86%, respectively. These results were achieved when using Random Forest for classification. This study also pointed to the possibility of effectively detecting diseases through blood tests using classic intelligent classifiers.
In our previous work (Barbosa et al., 2020a) we proposed an intelligent system to aid Covid-19 diagnosis using blood exams. After testing several machine learning methods, we achieved high classification performance using Bayes network as classifier. Our system was built using a public database from the Hospital Israelita Albert Einstein, in Brazil (Kaggle, 2020) and showed average 185 accuracy of 95.159% ± 0.693, with kappa index of 0.903 ± 0.014, sensitivity of 0.968 ± 0.007, precision of 0.938 ± 0.010 and specificity of 0.936 ± 0.011. In this study we were able to minimize costs by selecting only 24 blood tests from the set of 107 available exams. However, although we managed to achieve good results, the set of selected tests did not considered all the exams indicated by Similarly, the study by Soares et al. (2020) uses a method based on artificial intelligence to identify Covid-19 through blood tests. As in our previous work (Barbosa et al., 2020a) , they used the database from the Hospital Israelita 195 8 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10. 1101 Albert Einstein. However, since the database has many missing data, they chose to include only the subjects that had most of the data. This procedure reduced the dataset from 5,644 samples to 599 samples. By using Support Vector Machines as a classifier and SMOTEBoost technique to perform oversampling, they achieved average specificity of 85.98%, negative predictive value (NPV) of These intelligent systems based on blood tests may play an important role in the process of diagnosing Covid-19, since many studies are confirming evidences of this disease on blood. Moreover, as previously mentioned, most of these exams 205 are simple, fast and widely available. Based on this initial classification using blood tests, positive cases can be referred to further highly sensitive testing such as RT-PCR with virus DNA identification, CT scan and Radiography.
In this sense, many other studies are also being conducted in order to optimize Covid-19 diagnosis using these other testing methods. Gomes et al. 210 (2020c) proposed a new technique for representing DNA sequences to optimize the molecular diagnosis of Covid-19. Their method divides the DNA sequences into smaller sequences with overlap in a pseudo-convolutional approach, and represented by co-occurrence matrices. The DNA sequences are obtained by the RT-PCR method, eliminating sequence alignment. Through this approach, 215 it is possible to identify virus sequences from a large database by using AI tools to improve both specificity and sensitivity. The group conducted experiments with 347,363 virus DNA sequences from 24 virus families and SARS-Cov-2.
They used three different scenarios to diagnose SARS-Cov-2. In the first scenario the authors used all 24 families and SARS-Cov-2. They achieved 0.822222 220 ± 0.05613 for sensitivity and 0.99974 ± 0.00001 for specificity when using Random Forests with 100 trees and 30% overlap. For the second scenario they aimed to compare SARS-Cov-2 with similar-symptoms virus families. In this condition, MLP classifier with 30% overlap performed better, showing sensitivity of 0.97059 ± 0.03387 and specificity of 0.99187 ± 0.00046. Finally, in the third 225 scenario, they tested the real test scenario, in which SARS-Cov-2 is compared 9 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. .
to Coronaviridae and healthy human DNA sequences. For this last condition they found 0.98824 ± 001198 for sensitivity and 0.99860 ± 0.00020 for specificity with MLP and 50% overlap.
Another study from our group (Gomes et al., 2020b) shows benefits of us-230 ing artificial intelligence to perform automatic detection of Covid-19 in X-ray images. In this work, we proposed an intelligent system able to differentiate Covid-19 from viral and bacterial pneumonia in X-ray images, with low computational cost. There are many advantages of using X-rays images in Covid-19 diagnosis process: it is a widely available technique, with low cost, and fast 
In this work we present an intelligent system in web format called Heg.IA.
Heg.IA is a system that seeks to optimize the diagnosis of Covid-19 through 245 the use of artificial intelligence. The basic idea is that healthcare professionals can log in to the system and register patients in care. Then, it is possible to insert the results of blood tests that are commonly requested for patients with characteristic symptoms of Covid-19. After completing the filling, the professional can view and print the report. The report will indicate from the 250 blood tests if the patient is infected with the SARS-Cov2 virus. In addition, it will also indicate whether the patient should be admitted to the regular ward, Semi-intensive care unit (Semi-ICU) or Intensive care unit (ICU). These results will be accompanied by the values of accuracy, sensitivity, specificity and kappa 10 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint index, helping the physician in making the final decision. The diagram in the Figure 1 shows a summary of this solution. Figure 1 : General proposed method: The main idea is for a patient with symptoms characteristic of Covid-19 to go to a health center. He will be evaluated by a medical team, who must order blood tests. After obtaining the results, a health professional will be able to access the HegIA website. On the website, he must log in. Then he can enter the patient's blood test results. After finishing, the system will generate a report with a positive or negative diagnosis for Covid-19, in addition to the hospitalization prediction. This report can be printed and used for the medical team to define the final clinical conduct.
It is important to note that health professionals actively participated in the process of developing the system's front-end. Thus, the interface of the developed system is easy to use and does not require long periods of user training.
Furthermore, the developed back-end uses conventional classifiers. These choices 260 made it possible to achieve a low computational cost, and to make the result available almost immediately.
In addition to the functionality of registering patients, the website also allows access by the other physicians who have not participated in the blood test analysis process. This access will be through a patient's private locator. For this 265 type of user, only the report's visualization and printing functions are available.
These system use cases are described in the diagram in the Figure 2 .
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint Figure 2 : Use case Diagram: The main users of the system are nurses and medical laboratory professionals. They will be able to register patients, insert patients' blood test results and check if the data was entered correctly. They can also generate diagnostic reports and consult them at any time. Physicians will be able to view the report, accessing the system through a patient's personal locator.
In this work, we used a public database with information on hospitalized patients at Hospital Israelita Albert Einstein, located in São Paulo (Brazil) 270 (Kaggle, 2020) . The database consists of data from 5644 patients with symptoms similar to Covid-19. All patients' personal information was omitted from the database, respecting privacy, with the exception of the age. Patients underwent multiple clinical examinations, including blood tests, arterial and venous blood gas analysis, urine tests and rapid tests for various types of viruses. The exams 275 total 107 clinical parameters. In addition, all patients were tested for Covid-19 by analyzing swabs by RT-PCR with DNA sequencing, the current gold 12 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint standard for the disease. Among the patients, 559 tested positive for Covid-19.
The database also presented information regarding the hospitalization of each patient, indicating whether they were treated in the regular ward, semi-intensive 280 care unit or in the ICU.
In the database, not all 5644 patients underwent all types of clinical examinations. Therefore, the base has several missing values. In addition, several exams or attributes are categorical, that is, they have nominal results (or classes). As 285 the objective of this work is to use this data as input parameters for machine learning methods, it was necessary to handle the missing data and transform the categories into numerical classes. The transformations made are described in the following Table 1 . The left column indicates the attributes that have been modified, while the right column indicates the numerical values assigned to each 290 category. For the case of the result of pathological tests for SARS-Cov2, for instance, it was assigned the value 0 for not detected cases, 1 when abnormalities are detected, and 2 for missing values.
Finally, it was also necessary to treat the missing data from the columns with numerical classes. In these cases, a value of zero has been assigned. As 295 the base is normalized with a mean of zero and standard deviation of 1, this transformation means that the missing data were filled in with the average value of each of the exams. This assumption is valid, since 90.1% of the patients in the database have a negative result for Covid-19, and therefore, their parameters may be considered normal. 
Among the 107 exams available in the initial database, 41 exams were selected. The exams chosen correspond to those recommended by the Ministry of Health of Brazil as an initial clinical approach and part of the Covid-19 diagnostic process (Brazilian Ministry of Health, 2020). Thus, considering that health 305 centers must already perform these tests, there is no financial loss or time spent 13 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint Table 1 : Database pre-processing: The attributes with categorical classes were reorganized, receiving numerical values corresponding to each of the classes. The attributes related to pathological exams, such as SARS-Cov2, types of influenza and parainfluenza, received the values of 0, 1 and 2 for the absence of pathogens, presence of pathogens, and missing values, respectively. The attributes "Unine tests", "Urine Crystals", "Unire color", "urine urobilinogen" and "urine leukocytes" also have been modified, according to the labels indicated in right column.
on additional tests. On the contrary, the diagnostic process can be optimized with the system proposed here.
The list of 41 hematological parameters is shown in the Figure 3 . The Complete Blood Count (CBC) with differential comprises 20 of these parameters, CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint Multilayer Perceptron networks, on the other hand, have several interconnected neurons (or nodes), arranged in layers: the input layer, the hidden layers and the output layer. The input layer only has the network input vector, which is passed on to the next layer. Then, each node in the next layer modifies these input values through non-linear activation functions, generating output signals.
In addition, the network nodes are connected by weights, which scales these output signals. Finally, the superposition of several non-linear functions allows 15 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . the mapping of the input vector to the output vector. As MLPs can have one or multiple hidden layers, this process can be repeated several times, depending on the selected architecture (Gardner & Dorling, 1998; Lerner et al., 1994; Phung 330 et al., 2005; Barbosa et al., 2020b) .
Thus, through the proper selection of activation functions and synaptic weights, an MLP is able to approximate the inputs at the desired outputs.
This search and adjustment of parameters is called the training process. MLPs learn in a supervised manner. During this process, errors between the actual 335 and desired outputs are calculated. These errors are used to adjust the network (Gardner & Dorling, 1998) .
In order to adjust these weights, the backpropagation algorithm is the most computationally straightforward and common algorithm. It occurs in two phases: the forward and backward propagation. In the first step, the initial network 340 weights are set to small random values. Then, this first input vector is propagated through the network to obtain an output. This actual output is compared with the desired one, and the error is calculated. In the second phase, the backward propagation, the error signal is propagated back through the network and the connection weights are updated, aiming to minimise the overall error. These 345 steps can be repeated until the overall error is satisfactory (Haykin, 2001) .
MLPs and other artificial neural networks architectures are commonly used in support diagnosis applications (Naraei et al., 2016) , e.g. liver disease dianogis (Abdar et al., 2018), heart diasese diagnosis (Hasan et al., 2017) , breast cancer diagnosis over breast thermography (de Vasconcelos et al., 2018; Pereira 350 et al., 2020b; Santana et al., 2020; Pereira et al., 2020a,c; Santana et al., 2018; Rodrigues et al., 2019) and mammography images (de Lima et al., 2016; Lima et al., 2015; de Lima et al., 2014; Silva et al., 2020; Cordeiro et al., 2017 Cordeiro et al., , 2016 de Lima et al., 2014; Cruz et al., 2018) , for recognition of intracranial epileptic seizures (Raghu & Sriraam, 2017) , and multiple sclerosis diagnosis support 355 (Commowick et al., 2018) .
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint
Support Vector machines (SVM) were created by Vladimir Vapnik and Alexey Chervonenkis (Boser et al., 1992; Cortes & Vapnik, 1995) in 1963. Their main purpose is to build a linear decision surface, called a hyperplane. The idea is 360 that this hyperplane should be able to separate classes in the best possible way.
The optimal hyperplane is found when the margin of separation between it and a given nearest point is maximum (Haykin, 2001) .
SVM is known for its good generalization performance. Therefore, it is used in several healthcare applications, such as breast cancer diagnosis using 365 thermography and mammography (de Vasconcelos et al., 2018; Pereira et al., 2020b; Santana et al., 2020; de Lima et al., 2016; Lima et al., 2015; de Lima et al., 2014; Silva et al., 2020; Cordeiro et al., 2017 Cordeiro et al., , 2016 de Lima et al., 2014; Cruz et al., 2018) , diabetes mellitus diagnosis (Barakat et al., 2010) , heart valve diseases (Çomak et al., 2007) and pulmonay infections detection (Yao et al., 370 2011), and also diagnosis of pulmonary cancer (Sun et al., 2013) . However, its performance varies depending on the problems complexity. The type of the machine varies with the type of kernel used to build the optimal hyperplane. Table 2 shows the kernel functions used in this study: the polynomial and RBF kernels. For the first case, it was tested exponents of value 1 (linear kernel), 2, 375 and 3.
Polynomial 
Decision trees are sequential models, which combine several simple tests.
They can be understood as a series of questions with "yes" and "no" answers.
These tests can be the comparison of a value with a threshold or a categorical 380 attribute compared to a set of possibilities, for instance. Thus, when analyzing 17 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint the data with these tests, the decision trees will guide to a certain class in classification problems, or to a continuous value, in cases of regression problems. In this way, a decision tree is built with certain questions, called nodes. Essentially, there area four types of nodes: root, parent, child, and leaf. Starting at the root 385 node, an instance is classified. Then, the outcome for this instance is determined ad the process continues through the tree. In addition, one node may connect to another, establishing a parent-child relationship, in which a parent node generate a child node. Finally, the terminal nodes of the tree are the leaf nodes, and they represent the final decision, that is, the predicted class or value.
There are several types of decision trees, depending on the tree structure. The most popular ones are Random Tree and Random Forest. Both of them were tested in this study by using multiple parameters (Kotsiantis, 2013; Podgorelec et al., 2002) . 
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint On the other hand, the Naive Bayes classifier is a simple model that considers that the domain variables are conditionally independent, that is, one characteristic is not related to the other. Its learning is done in an inductive way, presenting a set of training data and calculating the conditional probability 415 of each attribute, given a class. Naive Bayes needs to estimate few parameters (Cheng & Greiner, 2001; Bouckaert, 2008) .
All experiments were performed using the Weka software. The experiments were made by using the following techniques: SVM with polynomial kernel of 420 degree (E) 1, 2, and 3 and RBF kernel with γ of 0.01; MLP with 50 and 100 neurons in hidden layer; Random Forest with 10, 20, 30, . . . , 100 trees; Random Tree; Naive Bayes; Bayesian Network. As a evaluate method we choose to perform a 10-fold cross validation and each configuration was executed 25 times.
We chose seven metrics to evaluate the performance of diagnostic tests: accuracy, precision, sensitivity, specificity, recall, precision and the area under ROC. Accuracy is the probability that the test will provide correct results, that is, be positive in sick patients and negative in healthy patients. In other words, it is the probability of the true positives and true negatives among all the re-430 sults. The recall and sensitivity metrics can be calculated mathematically in the same way. They are the rate of true positives, and indicate the classifier ability to detect correctly people with Covid-19. However, they are commonly used in different contexts. In machine learning context, the term Recall is common. However, in the medical world, the use of the sensitivity metric is more 435 frequent. Precision, on the other hand, is the fraction of the positive predictions that are actually positive. Specificity is the capacity of classifying healthy patients as negatives. It is the rate of true negatives. The Kappa index is a very good measure that can handle very well both multi-class and imbalanced class problems, as the one proposed here.
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020 . . https://doi.org/10.1101 Finally, the area under the ROC curve is a measure of a classifier's discriminating ability. That is, given two classes -a sick individual and a non-sick individual -, chosen at random, the area below the ROC curve that indicates a probability of the latter being correctly classified. If the classifier can not discriminate between these two separately, an area under a curve is equal to 445 0.5. When this value is the next 1, it indicates that the classifier is able to discriminate these two cases (Hand, 2009 ) .
These metrics allow to discriminate between the target condition and health, in addition to quantifying the diagnostic exactitude (Borges, 2016). The accuracy, precision, sensitivity, specificity, recall and precision can be calculated 450 according to the equations in Table 3 . In Table 3 , TP is the true positives, TN is the true negatives, FP is the false positives, and FN the false negatives, ρ o is observed agreement, or accuracy, and ρ e is the expected agreement, defined as following:
(1) 20 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . This section presents the results obtained for the evaluation of different computational methods to detect SARS-Cov2 and to indicate the type of hospitalization. Furthermore, we show the main screens of our web system.
Since the proposed system aims to perform the tasks of diagnosing Cov2 and recommending the type of hospitalization, we performed the classification tests considering each scenario. In Table 4 
In the graphs shown in Figures 5 to 7 Unit or Regular Ward. Figure 5 shows the results of accuracy and kappa statistic for the Intensive Care Unit. Figures 6 and 7 present the achieved results for Semi Intensive Care Unit and Regular Ward, respectively.
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint In Table 5 
After selecting the best classifier, the Heg.IA web system was developed.
It can be accessed through the link: http://150.161.141.202/welcome. Its front-end was developed using the library React.js. This library is based on 485 pure JavaScript. It is open source and used to create user interfaces, more 22 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint specifically, single page application (SPA) web platforms. As for data access and manipulation of application state, we used the Redux-Saga structure, a powerful tool that allows us to manage masterfully asynchronous queries, receiving API data, and trigger actions to the application of state safely and easily to maintain.
Furthermore, our back-end was developed in Python (version 3.7.7). Only the Random Forest classifier was implemented in this final solution.
On the initial screen, as shown on Figure 8 , it is possible to visualize a brief description of the intelligent system, as well as the supporters of this initiative: The Federal University of Pernambuco (UFPE) and the Department of 495 23 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint Biomedical Engineering at UFPE. To get to know the members of the project's development team and their respective functions, it is possible to access the 'About' option on the top menu of the screen. The options 'Login' and 'Consult' are also available. For the 'Login' option, health professionals, especially medical laboratory professionals and nurses, will be able to access their private 500 account or register a new account, in cases of first access. In the Consult option, it is possible to view the report with the diagnosis for a specific patient, as long as the user has the patient's personal locator.
After logging into the system, the user can register a patient or view the 24 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint complete history of registered patients. In the case of a new registration, per-505 sonal information such as full name, ID, date of birth, telephone, sex and full home address will be requested (Figure 9 ). In the following, the user will be directed to the screen shown in the Figure 10 . In this screen, the results of the Complete blood count (CBC) with differential must be entered. The units and reference values are available next to each of the hematological parameters.
After filling in the CBC, the user will be directed to the screens for the other blood tests and arterial blood gas tests, as shown in the Figures 11 and 12. Thus, the list of tests required to make the predictions will be complete. The
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. user can then check the parameters entered in the screen 'Let's check it out?', as shown in the Figure 13 . If he realizes that he made a typo, he can go back 515 to the previous steps and correct it.
Finally, the report will be available immediately, similarly to that shown in the Figure 14 . The diagnostic report will indicate the positive or negative diagnosis for Covid-19. Hospitalization predictions are also reported, indicating the best type of hospitalization for the patient: regular ward, semi-ICU or 520 ICU. Information on accuracy, kappa index, sensitivity and specificity of the determination of each of these scenarios are also available, in order to assist the physician's decision making. In addition to viewing the report, it is also possible to print it.
For the experiments comparing different classification models, Figures 4, 5, 6 and 7 show that, overall, Random Forest method overcame the others in both accuracy and kappa statistic. When using 90 trees in the Random Forest, the method achieved great results for all scenarios, always reaching accuracy above 90% and kappa statistic above 0.80. From these results we also found that the 530 task of diagnosing SARS-Cov2 from the used blood tests (Figure 3) showed to be harder than indicating the type of hospitalization using the same group of exams.
For SARS-Cov2 detection scenario, all tested configurations of Random Forest and MLP showed good performance, with accuracies above 90% and kappa 535 above 0.80. Less satisfying results were achieved by the SVMs, Bayesian networks and Random Tree. These last classifiers reached accuracy values between 26 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020 June 16, . . https://doi.org/10.1101 June 16, /2020 doi: medRxiv preprint Figure 8 : Heg.IA homepage: an intelligent web-based system for diagnosing Covid-19 through blood tests. On this initial screen, it is possible for the user (nurses and medical laboratory professionals) to login in his personal account. There are also the 'Consult' and 'About' options, available for all users, including patients and physicians. They allow the user to visualize the diagnostic report, and to get to know the involved team in this project, respectively. 80% and 90% with kappa statistic varying from 0.60 to 0.80. As for data dispersion, it was slightly greater for kappa results, when compared to accuracy.
However, both graphs show low dispersion, indicating good reliability of the 540 decision of all algorithms. The dispersion of Bayesian networks and Random Tree were slightly bigger than the other methods.
When regarding to classifiers performance on indicating the type of hospitalization, we found some outstanding results, specially using Random Forest with 90 and 100 trees. These classifiers achieved similar results for all three types of 545 hospitalization. However, Random Forest with 90 trees performed slightly better for Regular Ward indication. In this scenario, once more, we achieve great results using MLP and Random Forest, with all results around 100% for accuracy and around 1.00 for kappa statistic. Impressive results were also reached using SVM with linear kernel, Naive Bayes network and Random Tree, all sim-550 ilar to the results using Random Forest and MLP. SVM with polynomial kernel 27 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020 June 16, . . https://doi.org/10.1101 June 16, /2020 doi: medRxiv preprint Figure 9 : On this screen, the logged in user will be able to register new patients or access the complete history of patients already registered. and Bayes network performed worse than the other methods, showing higher data dispersion but still achieved accuracy values above 96% and kappa above 0.90. The data dispersion for the other method were very low, reaching its lowest values for Random Forest with 90 and 100 trees.
From the previously mentioned results, we found that the best classifier to solve both SARS-Cov2 detection and hospitalization indication problems was the Random Forest with 90 trees. This decision was supported by the excellent results shown on Tables 4 and 5. These table shows the method performance for the most relevant metrics regarding to diagnosis quality. For all scenarios 560 described in both tables, we found accuracy, kappa, recall, sensitivity, precision, specificity and area under ROC curve really close to the their maximum values.
These results indicate that the system showed a great overall efficiency. Considering this performance, we chose to use this model to build our HegIA Web Application.
28 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020 . . https://doi.org/10.1101 
The disease caused by the new type of coronavirus, the Covid-19, has posed a global public health challenge. Since this virus has a stronger human-tohuman transmission ability, it has already led to millions of infected people and thousands of deaths since the beginning of the outbreak in December 2019. As 570 pointed out by the World Health Organization, testing is our currently best strategy to fight Covid-19 pandemic spread.
The ground-truth test in Covid-19 diagnosis is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) with DNA sequencing and identification. RT-PCR is precise, but takes several hours to be assessed. Another type 575 of test, based on IgM/IgG antibodies, delivers results quickly, however they are nonspecific for Covid-19, and may have very low sensitivity and specificity.
IgM/IgG tests do not directly detect the SARS-Cov2 presence, indeed they detect the serological evidence of recent infection. Considering this, the development of a diagnosis support system to provide fast results with high sensitivity 580 and specificity is necessary and urgent. In this context, blood tests have some advantages. First, they are commonly used during medical screening. Besides that blood tests are less expensive and less time-consuming than other diagnosis methods. Thus providing a more accessible system.
In this study, we developed a web system, Heg.IA, which seeks to optimize 585 the diagnosis of Covid-19 by combining blood tests, arterial gasometry results and artificial intelligence. From the system, a healthcare professional may have a diagnostic report after providing 41 hematological parameters from common blood tests. The system will indicate if the patient is infected with SARS-Cov2 virus, and also recommend the type of hospitalization (regular ward, semi-ICU, 590 or ICU).
The proposed system is based on decision trees and achieved great performance of accuracy, kappa statistic, sensitivity, precision, specificity and area under ROC for all tested scenarios. Considering SARS-Cov2 detection, the system may play an important role as a highly efficient rapid test. The hospi-595 29 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint talization recommendation module of the system can be important to speed up and improve decision-making regarding the referral of each patient.
Heg.IA application may be a way to overcome the testing unavailability in the context of this pandemic. Moreover, the hospitalization prediction can also support decision regarding to patient referral according to the hospitalization 600 conditions of each health institution. Since the system is flexible and available online, we expect to reach different nations of the globe, specially less-favored countries and communities, in which the absence of testing is even more critical. Finally, we hope the system will provide wide access to Covid-19 effective diagnosis and thereby reach and help saving as many people as possible.
Brazilian Ministry of Health (2020).
Guidelines for the diagnosis and treatment of COVID-19 .
Brazilian Society of Clinical Analyzes. URL: www.sbac.org.br/blog/2020/04/09/ diretrizes-para-diagnostico-e-tratamento-da-covid-19/ last ac-. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint ology for classification of lesions in mammographies using zernike moments, elm and svm neural networks in a multi-kernel approach. In 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 988-91).
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. Lesion detection in breast thermography using machine learning algorithms 36 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 16, 2020. . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 16, 2020. 
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint Figure 10 : Complete Blood Count screen: After the patient's registration, the results of the patient's Complete Blood Count with differential can be inserted. The units and reference values can be viewed next to each hematological parameter.
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint 
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint 
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint Figure 13 : In the screen, it is possible for the user to check the patient's personal information and the values of the hematological parameters inserted. If there is a typo, the user can return to the previous screens to correct it. If they are correct, it is possible to select the "Finish" option in order to access the report.
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10.1101/2020.06.12.20129866 doi: medRxiv preprint Figure 14 : Results screen: In this screen it is possible to view the patient's diagnostic report.
In the report, the diagnosis for Covid-19 is available, as well as the hospitalization predictions, indicating whether the patient should be admitted to the regular ward, semi-intensive care unit, or to the ICU. Information on accuracy, kappa index, sensitivity and specificity of the determination of each of these scenarios are also available, in order to assist the physician's decision making. In addition to viewing the report, it is also possible to print it.
. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 16, 2020. . https://doi.org/10. 1101 