Deep Learning for COVID-19 Prediction based on Blood Test

Ziyue Yu

, Lihua He

, Wuman Luo

1,2

, Rita Tse

1,2

and Giovanni Pau

3,1,4

School of Applied Sciences, Macao Polytechnic Institute, Macao, SAR, China

Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence

of Ministry of Education, Macao Polytechnic Institute, Macao, SAR, China

Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

UCLA Computer Science Department, Los Angeles, U.S.A.

Keywords: Covid-19, Deep Learning, Blood Test, CNN+BI-GRU.

Abstract: The COVID-19 pandemic is highly infectious and has caused many deaths. The COVID-19 infection diagno-

sis based on blood test is facing the problems of long waiting time for results and shortage of medical staff.

Although several machine learning methods have been proposed to address this issue, the research of COVID-

19 prediction based on deep learning is still in its preliminary stage. In this paper, we propose four hybrid

deep learning models, namely CNN+GRU, CNN+Bi-RNN, CNN+Bi-LSTM and CNN+Bi-GRU, and apply

them to the blood test data from Israelta Albert Einstein Hospital. We implement the four proposed models

as well as other existing models CNN, CNN+LSTM, and compare them in terms of accuracy, precision, recall,

F1-score and AUC. The experiment results show that CNN+Bi-GRU achieves the best performance in terms

of all the five metrics (accuracy of 0.9415, F1-score of 0.9417, precision of 0.9417, recall of 0.9417, and AUC

of 0.91).

1 INTRODUCTION

The Coronavirus Disease (COVID-19) is a global

pandemic with high infectiousness and fatality rate.

According to Johns Hopkins University (Johns Hop-

kins University, 2021), as of January 9, 2021, the

worldwide COVID-19 death toll has passed 1.9 mil-

lion, and the number of confirmed cases has exceeded

88.9 million. To make matters worse, the newly

emerged COVID-19 variants are 70% more infec-

tious than the original virus (World Health Organiza-

tion, 2020). Facts have proved that “early detection,

reporting, isolation and treatment” is the most effec-

tive way to prevent the rapid spread of the virus and

minimize the infected number (World Health Organ-

ization, 2020). Therefore, frequent routine test plays

a critical role in the battle against COVID-19.

Typically, there are two important ways of

COVID-19 routine tests, i.e., the blood test and the

nucleic acid test. The nucleic acid is currently widely

used because of its simplicity. However, its false-neg-

ative rate can be as high as 20%. The blood test out-

performs the nucleic acid test in that its false positives

and false negatives are much smaller than those of the

nucleic acid test (Ferrari et al, 2020). The blood test

can not only effectively avoid missing true positive

cases but detect seasonal coronaviruses patients with

false-positive results, thereby avoiding unnecessary

isolation (Peeling et al, 2020). However, pure medical

approach for the blood test is confronted with two ma-

jor problems. First, the blood test is time-consuming

and usually takes several days to get the test result

(World Health Organization, 2020). Second, the

shortage of medical staff for COVID-19 blood test is

very common (Wynants et al, 2020), especially in de-

veloping countries. As a result, patients usually have

to wait a long time (sometimes as long as several

weeks) after the blood is drawn to get the results

(Amanda et al, 2020).

To address these issues, we adopt deep learning

approaches for COVID-19 prediction based on the

blood test. The objective is to relieve the medical staff

from the heavy testing work and speed up the testing

process. So far, artificial intelligence is becoming in-

creasingly important in the area of medical diagnosis

(He et al, 2019). However, the research of COVID-19

prediction based on deep learning is still in its prelim-

inary stage. In 2020, Alakus et al. applied six deep

learning models to the blood test data from Hospital

Israelita Albert, among which the hybrid model of

Yu, Z., He, L., Luo, W., Tse, R. and Pau, G.

Deep Learning for COVID-19 Prediction based on Blood Test.

DOI: 10.5220/0010484601030111

In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security (IoTBDS 2021), pages 103-111

ISBN: 978-989-758-504-3

103

convolutional neural network (CNN) and Long Short-

Term Memory (LSTM) achieved the best prediction

accuracy of 92.3% (Alakus et al, 2020). Other pro-

posed machine learning methods for COVID-19 pre-

diction based on blood test include decision trees

(DT), random forests (RF), support vector machines

(SVM), logistic regression (LR). However, the pre-

diction accuracies of these machine learning models

are suboptimal (around 80-85%) compared with the

deep learning methods (Jiang et al, 2020) (Batista et

al, 2020) (Cabitza et al, 2020).

In this paper, we explore deep learning methods

for COVID-19 prediction and propose a prediction

system which contains four hybrid deep learning

models including CNN+Gated Recurrent Unit

(GRU), CNN+Bidirectional RNN (Bi-RNN),

CNN+Bidirectional Long Short-Term Memory

(CNN+Bi-LSTM), CNN+Bidirectional GRU

(CNN+Bi-GRU). The blood test data are from Hospi-

tal Israelita Albert Einstein and have been used in the

work of Alakus et al (Alakus et al, 2020). We evaluate

the performance of the proposed system in terms of

accuracy, precision, F1-Score, recall and AUC. And

the experiment results show that the proposed hybrid

model CNN+Bi-GRU outperforms the best model

(CNN+LSTM) proposed by Alakus et al (Alakus et al,

2020) in terms of the five evaluation metrics. In sum-

mary, the main contributions of this paper are as fol-

lows:

• We design and implement four hybrid deep

learning models including CNN+GRU,

CNN+Bi-RNN, CNN+Bi-LSTM and

CNN+Bi-GRU for COVID-19 prediction. And

we use 18 attributes of blood test data from

Hospital Israelita Albert Einstein for model

training.

• We also implement two models proposed by

Alakus et al (Alakus et al, 2020), i.e., CNN and

CNN+LSTM, and conduct extensive compari-

sons among these six models in terms of accu-

racy, precision, F1-Score, recall and AUC. The

blood test data used in this work are from Hos-

pital Israelita Albert Einstein provided by Ala-

kus et al (Alakus et al, 2020).

• The experiment results show that the values of

accuracy, precision, F1-Score, recall and AUC

of the proposed hybrid model CNN+Bi-GRU

are 0.9415, 0.9417, 0.9417, 0.9417 and 0.91,

respectively, which are better than those of the

best model (CNN+LSTM) proposed by Alakus

et al (Alakus et al, 2020).

The rest of this paper is organized as follows. Re-

lated work of our research will be given in Section 2.

Section 3 describes the blood test data and the pro-

posed deep learning models. We provide a thorough

experiment study and performance comparison in

Section 4. In Section 5, we give a conclusion of the

paper.

2 RELATED WORK

Nowadays, AI plays an important role in assisting

medical diagnosis. For example, CNN could be used

for the early detection of cancer (Dlamini et al, 2020).

Recurrent neural network (RNN) used for the diagno-

sis of Alzheimer’s disease (Cui et al, 2020). For

COVID-19 prediction, existing AI approaches can be

classified into two categories, namely general ma-

chine learning methods and deep learning methods.

Both machine learning and deep learning can be used

to solve classification problems.

2.1 Machine Learning

In 2020, Jiang et al. proposed five machine learning

models including LR, DT, RF, K-Nearest-Neighbor

(KNN) and SVM for COVID-19 prediction. They ap-

plied the models to the data from Wenzhou Central

Hospital and Cangnan People’s Hospital. The data

contain 53 blood samples from 53 hospitalized pa-

tients, each of which has 10 blood indicators (model

features) (Jiang et al, 2020). The core idea of LR is to

use existing data to establish a regression equation for

the classification. DT is a model used to observe and

realize the internal laws of data and to classify and

predict results for new data. RF inherits the idea of

DT. Differently, it uses the method of ensemble learn-

ing in which RF votes for the classification results of

several weak classifiers to form strong classifiers.

The idea of KNN is to calculate and compare the dis-

tances between the target point and the points of dif-

ferent categories in a given interval. And the category

of the target point will be determined by its k nearest

neighbors. SVM is a popular classification approach.

In 0-1 classification, SVM calculates the maximum

margin between two types of labeled data, based on

which establishes a hyperplane for classification. The

experiment results show that the best accuracy was

80% when using SVM. Besides the work of Jiang et

al., Batista et al. applied SVM, RF, LR and gradient

boosted trees for COVID-19 prediction based on the

data from Hospital Israelita Albert Einstein at Sao

Paulo Brazil (Batista et al, 2020). The number of pa-

tient samples is 235, and the number of blood indica-

tors (model features) for each sample is 13. Specifi-

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104

cally, gradient boosted trees use DT as the basis func-

tion, and the model is built in the direction of the gra-

dient drop of the loss function for each time. The best

accuracy of their proposed models was 84.7% when

using RF. Another research team from Italy used the

data from San Raffaele Hospital with 1,925 patients

from February 2020 to May 2020. They selected 20

blood indicators as the model features in their study

(Cabitza et al, 2020). Their proposed models were LR,

Naïve Bayes, KNN, RF, and SVM, and the best ac-

curacy rate achieved 88% when using RF model.

2.2 Deep Learning

To the best of our knowledge, the research for deep

learning-based COVID-19 prediction is still in its pre-

liminary stage. One typical work is the prediction sys-

tem proposed by Alakus et al (Alakus et al, 2020).

This system contains six deep learning models includ-

ing artificial neural networks (ANN), CNN, RNN,

LSTM, CNN+RNN and CNN+LSTM. ANN refers to

the input layer, the hidden layer and the output layer

are in the form of full connection. CNN is widely used

in various scenarios such as image processing, text

processing and speech recognition. It contains the

convolutional layer, the pooling layer and the fully

connected layer. The convolutional layer is used for

extracting features, and the pooling layer can speed

up calculation and prevent overfitting. In addition,

shared weights and biases refer to the sharing of

weight parameters in the process of convolution and

pooling, which makes the model easier to optimize.

In summary, the main idea of CNN is to learn the spa-

tial hierarchies of features through backpropagation

by using multiple building blocks. RNN is suitable for

processing time series data, this model adds a hidden

state for recording historical information, and the ac-

tivation function uses Tanh to prevent the value from

changing too severely. LSTM includes forget gate, in-

put gate, output gate and the memory cell. The forget

gate refers to the information that needs to be forgot-

ten, the input gate refers to the information that needs

to flow into the memory cell, and the output gate re-

fers to the information that needs to flow into the hid-

den state. Memory cells are used to remember histor-

ical information. And the reason why the combination

of CNN and other deep learning models can usually

achieve better accuracy is that CNN has performed

feature extraction on the data in advance. Alakus et al.

used the blood test dataset including 600 patients

from Hospital Israelita Albert Einstein at Sao Paulo

Brazil, and selected 18 features for model training and

testing. The best accuracy achieved 92.3% when us-

ing CNN+LSTM, which is higher than existing ma-

chine learning models.

3 COVID-19 PREDICTION USING

DEEP LEARNING MODELS

Based on the work of Alakus et al., we propose and

implement four hybrid models for COVID-19 predic-

tion using blood test data. In this section, we intro-

duce the four proposed models, which share the same

general structure. This structure contains three convo-

lution layers, two MaxPool layers, one recurrent neu-

ral layer and one fully connected layer. The major dif-

ference between the four models is that they have

adopted four different variation of the recurrent neu-

ral layer, respectively, i.e., GRU, Bi-RNN, Bi-LSTM,

Bi-GRU. We set the same parameters for the four

models for the sake of fairness.

3.1 CNN+GRU

The first proposed hybrid model is CNN+GRU. GRU

is a variation of RNN. The hybrid model of CNN and

RNN was proposed in the work of Alakus et al. CNN

and RNN are complementary to each other in model-

ing capabilities (Sainath et al, 2020). Specifically,

CNN can extract features of different levels, while

RNN can provide short-term memory. Therefore,

combining them can improve the efficiency of diag-

nosis for COVID-19. However, one major problem of

RNN is gradient disappearance or explosion caused

by its iteration (Chung et al, 2014). To address this

issue, we propose GRU to replace RNN in the hybrid

model. The GRU network uses the cell to store infor-

mation, and the gated mechanism controls whether

the information needs to be retained in the cell. The

internal structure of GRU is shown in Figure 1. GRU

network has two gate control units, namely reset gate

𝑟

and update gate 𝑧. The reset gate controls whether

the information of historical state needs to be forgot-

ten. When the value of reset gate close to 0, the infor-

mation of historical state is forgotten, and the candi-

date state is only related to the current input. This

mechanism can discard some useless information to

reduce computational complexity. The update gate

controls whether the information of historical state

needs to be sent to the current state. When the value

of update gate value close to 0, the current state is

only related to the current input; when its value close

to 1, the current state is equal to the previous state.

Deep Learning for COVID-19 Prediction based on Blood Test

105

Figure 1: The Internal Structure of GRU.

3.2 CNN+Bi-RNN

The second proposed hybrid model is CNN+Bi-RNN.

The basic idea is to adopt the Bi-RNN to extract the

correlation between each different attributes of data.

Bi-RNN can increase contents in the memory of fu-

ture information (Schuster et al, 1997). This structure

allows the model to better understand the correlation

between current data and other data. The internal

structure of Bi-RNN is shown in Figure 2. It shows

that each training sequence has a forward network

and a backward network, both of which are RNN. The

two networks are connected with the output layer.

This structure provides the output layer with complete

data information about the past and future data.

Figure 2: The Internal Structure of Bi-RNN.

3.3 CNN+Bi-LSTM

The third proposed hybrid model is CNN+Bi-LSTM.

The basic observation is that, in order to simulate the

memory characteristics of biological networks, neu-

rons can use the current output information as the in-

put of the next neuron to form a ring network struc-

ture, which is a neural network with short-term

memory. LSTM is a good candidate to store short-

term memory, and this memory can be retained for a

long time.

Figure 3: The Internal Structure of LSTM.

Specifically, LSTM uses the memory cell to store

the information, and the gating mechanism helps the

model determine how much information passes in the

cell between a large time step (Sak et al, 2014). LSTM

includes the forget gate, input gate, output gate and

the memory cell, which are marked 𝑓, 𝑖, 𝑜, and 𝐶 in

the Figure 3. Forget gate controls whether infor-

mation from the long-time memory unit needs to be

forgotten at the last moment; input gate controls

whether input information is required to enter a long-

time memory unit; output gate controls whether infor-

mation from the long-time memory unit can be out-

putted; Cell is a storage unit for short-term memory.

The Bi-LSTM model is to add a forward network and

a backward network to a hidden layer of the tradi-

tional LSTM so that the correlation between the data

can be extracted.

3.4 CNN+Bi-GRU

The fourth proposed hybrid model is CNN+Bi-GRU,

which combines CNN and Bi-GRU networks. The

CNN+Bi-GRU uses CNN to extract features at differ-

ent layers from the data, and the Bi-GRU is used to

construct the correlation between each attributes of

blood test data. Figure 4 is the structure diagram of

the CNN+Bi-GRU network. Specifically, the input to

the network are the blood test data of each patient.

First, CNN network extracts the characteristics of the

blood attributes. Then, Bi-GRU extracts the correla-

tion between the attributes. Finally, SoftMax function

is adopted to predict whether COVID-19 is positive

or not. As shown in Table 1, the network has eight

layers: 1) three convolutional layers. The kernel size

of the convolutional layer is 256, 128 and 64 respec-

tively, after the first two convolutional layers, a drop-

out layer with 15% rate is used to prevent overfitting;

2) two pooling layers. The kernel size of the pooling

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106

layer is 3; 3) one Bi-GRU layer. The hidden state di-

mension is set to 256, and Batch Normalization is

added to reduce the internal covariate offset. The ob-

jective is to speed up the deep network training; 4)

one fully connected layer. A dropout layer with rate

of 15% is used for reducing the risk of overfitting, and

5) one output layer with SoftMax function. The out-

put value indicates whether the COVID-19 infection

is negative or positive.

Table 1: The Summary of CNN+Bi-GRU.

Layer Type Kernel Stride

1 Convolution1D 256 3

2 MaxPool 3 3

3 Convolution1D 128 3

4 MaxPool 3 3

5 Convolution1D 64 3

6 Bi-GRU 256 -

7 Fully Connected 256 -

8 SoftMax 2 -

Figure 4: The Structure of CNN+Bi-GRU.

4 EXPERIMENT EVALUATION

In this section, we conduct experiments based on real

data, which are produced by Hospital Israelita Albert

Einstein in Sao Paulo Brazil. We first describe the da-

taset, the experiment environment and the evaluation

metrics. Then we use five metrics to evaluate the per-

formance of the six deep learning models. Finally, we

compare the results with previous studies.

4.1 Data

We use the real data from Hospital Israelita Albert

Einstein in Sao Paulo Brazil. The original data are

provided by the research team from Schwab (Schwab

et al, 2020), and include 111 laboratory results of

5644 different patients. These data were collected in

2020 from patients’ blood samples for COVID-19 in-

fection testing. The prevalence rate of the selected

data was 13.3%. To protect the patients’ privacy, all

the personal information is abandoned. Each row of

data represents the blood test information of an indi-

vidual. According to previous research (Alakus et al,

2020), we select 18 attributes as the features of the

proposed deep learning models. The attributes of the

blood test are listed in Table 2:

Table 2: Attributes of Selected Attributes.

Hematocrit Hemoglobin Monocytes

Serum Glucose Neutrophils Platelets

Red blood Cells Lymphocytes Leukocytes

Basophils Eosinophils Urea

Sodium Creatinine Potassium

Proteina C

reativa mg/dL

Alanine

transaminase

Aspartate trans-

aminase

4.2 Experiment Environment

We design the deep learning models using Python and

Keras package. The experiments are conducted using

a desktop with an 8GB memory Intel Core i7-2.9GHz

processor and a 4GB NVIDIA GeForce 940MX

graphical processor. We set 80% of the data as the

training set and 20% as the test set. The learning rate

is 0.001, the batch size is 30 and the epoch is 200.

Deep Learning for COVID-19 Prediction based on Blood Test

107

Figure 5: Deep Learning Models ROC Curves.

Table 3: Performance of Different Deep Learning Models.

Model Accuracy F1-Score Precision Recall AUC

CNN 0.8843 0.8843 0.8843 0.8843 0.83

CNN+LSTM 0.8545 0.8545 0.8545 0.8545 0.86

CNN+GRU 0.9210 0.9209 0.9210 0.9210 0.90

CNN+Bi-RNN 0.8786 0.8786 0.8786 0.8786 0.84

CNN+Bi-LSTM 0.8885 0.8885 0.8885 0.8885 0.89

CNN+Bi-GRU 0.9415 0.9417 0.9417 0.9417 0.91

4.3 Evaluation Metrics

In this work, for the sake of fairness, we adopt the

same five metrics as Alakus et al (

Alakus et al, 2020)

for performance evaluation, i.e., accuracy, F1-score,

precision, recall and area under the receiver operating

characteristic (AUC).

To calculate the metrics, the classification results

of the models are summarized in the form of a confu-

sion matrix. The matrix contains four different kinds

of values: 1) True Positive (TP) which means positive

classes are predicted to be positive; 2) True Negative

(TN) which means negative classes are predicted to

be negative; 3) False Positive (FP) which means neg-

ative classes are predicted to be positive, and 4) False

Negative (FN) which means positive classes are pre-

dicted to be negative. These four values can be used

to calculate accuracy, precision and recall. F1-score

is calculated based on precision and recall (Hossin et

al, 2015).

Specifically, accuracy refers to the proportion of

the samples (TP+TN) which have been correctly clas-

sified among the total number of samples

(TP+TN+FP+FN). It is the most common evaluation

metric for the classification tasks. Precision is the ra-

tio of the TP that belong to the predicted positives

(TP+FP). It indicates whether the model has a good

ability to discriminate between positive and negative

samples. Recall refers to the TP among the sum of the

TP and FN. It indicates the ability of the model to

classify the relevant results correctly. F1-score refers

to the harmonic average of the precision and recall. It

is a comprehensive metric to judge the ability of the

classification model. AUC is a method to evaluate the

performance of classifier. If the AUC value exceeds

0.8, the classifier can be regarded as excellent classi-

fier. If the AUC value is above 0.9, the classifier can

be regarded as outstanding (Mandrekar et al, 2010).

The calculation formulas of accuracy, precision, re-

call and F1-score are shown below.

TP+TN

Accuracy =

TN + FP + FN +TP

(1)

Precision =

TP+ FP

(2)

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Figure 6: Comparison with CNN+Bi-GRU and CNN+LSTM.

Table 4: Comparison with Previous Studies.

Dataset Location Model Accuracy AUC F1-Score

Hospital Israelita Albert Einstein SVM, RF - 0.87 0.7200

Hospital Israelita Albert Einstein CNN+LSTM 0.9230 0.90 0.9300

Hospital Israelita Albert Einstein CNN+Bi-GRU 0.9415 0.91 0.9417

Recall =

TP+ FN

(3)

Precision Recall

F1= 2

Precision Recall

(4)

4.4 Result Analysis

To make comprehensive comparisons, we implement

the four proposed model as well as the two models

(CNN and CNN+LSTM) proposed in the work of

Alakus et al. (Alakus et al, 2020). Besides, we use five

metrics to evaluate the six deep learning models, re-

spectively.

The Receiver operating characteristic (ROC)

curves of the six models are shown in Figure 5. Typ-

ically, the x-value of the ROC curve is the false posi-

tive rate (FPR), and the y-value of the ROC curve is

the true positive rate (TPR). For the ROC curve, the

area enclosed by the curve and the coordinate axis

represents the classification performance of the

model. The closer the area is equal to 1, the stronger

the classification ability of the model is, and vice

versa. In addition, we use the AUC value to describe

the area under the ROC curve and the coordinate axis.

AUC means that, if the sample is randomly selected,

the probability of the model can classify it into correct

category. Figure 5 shows that CNN+Bi-GRU model

gets the best results among all the six models, whose

AUC value achieves 0.91. It means that the model can

effectively classify positive cases.

Table 3 presents the performances of different

models in terms of different metrics. Specifically, it

shows that the AUC values of all hybrid models are

higher than the model CNN in the diagnosis of

COVID-19. And the three proposed hybrid models

CNN+GRU, CNN+Bi-LSTM, CNN+Bi-GRU out-

perform the model CNN in terms of the five metrics.

For CNN+LSTM and CNN+GRU model, the experi-

ment results show that the CNN+GRU model signif-

icantly improved the accuracy, F1-score, precision,

recall and AUC over CNN+LSTM. The basic idea of

GRU and LSTM is to reduce the problem of gradient

disappearance while retaining long-term sequence in-

formation. But GRU has a simpler network structure

than LSTM, which can accelerate the training and

convergence of the network. So, the CNN+GRU

model has achieved better results in our experiments.

Comparing the experimental results of CNN+GRU

and CNN+Bi-GRU models, we find that the values of

the five metrics of Bi-GRU are all better than those of

GRU. This is because the training data contain 18 at-

tributes from blood test. The correlation between the

data attributes is therefore a very important feature.

The bidirectional structure of Bi-GRU can better

model the correlations between each blood attribute.

Therefore, CNN+Bi-GRU has better evaluation per-

formances than CNN+GRU.

Figure 6 shows the detailed comparisons between

CNN+Bi-GRU and CNN+LSTM. For the sake of

fairness, we adopt the experiment results of

CNN+LSTM from the paper of Alakus et al (Alakus

et al, 2020), which are much higher than those of our

CNN+LSTM experiment results. The comparison re-

sults show that our proposed model CNN+Bi-GRU

outperforms CNN+LSTM in terms of all the five met-

rics.

Table 4 shows the comparison between CNN+Bi-

GRU and the work of Batista et al and Alakus et al.

Deep Learning for COVID-19 Prediction based on Blood Test

109

Batista et al used SVM and RF, and the best results

are 0.87 of AUC and 0.72 of F1-score. According to

Alakus et al, the best accuracy, AUC and F1-score of

CNN+LSTM are 0.9230, 0.90 and 0.93, respectively.

Our proposed CNN+Bi-GRU model provides the best

performance whose accuracy, AUC and F1-score are

0.9415, 0.91 and 0.9417, both higher than SVM, RF

and CNN+LSTM. Overall, the performance of

CNN+Bi-GRU is better than the other existing mod-

els.

5 CONCLUSIONS

In this paper, four hybrid deep learning models are

proposed to predict COVID-19 infection based on

blood test, i.e., CNN+GRU, CNN+Bi-RNN,

CNN+Bi-LSTM and CNN+BiGRU. Besides, 18 in-

dicators from the blood test data are selected as fea-

tures, and five metrics are adopted to evaluate the

model performance, namely accuracy, F1-score, pre-

cision, recall and AUC. Experiment results show that

CNN+Bi-GRU model outperforms the proposes

models of Alakus et al in terms of all the evaluation

metrics. We believe that CNN+Bi-GRU model will

be an effective supplementary method for COVID-19

diagnosis based on blood test. In the future, we will

continue explore deep learning models for COVID-

19 prediction and design novel prediction models.

ACKNOWLEDGMENT

This work was supported in part by the Macao Poly-

technic Institute – Big Data-Driven Intelligent Com-

puting (RP/ESCA-05/2020).

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