Trends Identiﬁcation in Medical Care

es Sena

and Ana I. Pereira

Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Polit

ecnico de Braganc¸a, Braganc¸a, Portugal

Keywords:

Artiﬁcial Intelligence, Machine Learning, Support Vector Machine, Classiﬁcation Algorithm, Reliability.

Abstract:

Daily, health professionals are sought out by patients, motivated by the will to stay healthy, making numerous

diagnoses that can be wrong for several reasons. In order to reduce diagnostic errors, an application was devel-

oped to support health professionals, assisting them in the diagnosis, assigning a second diagnostic opinion.

The application, called ProSmartHealth, is based on intelligent algorithms to identify clusters and patterns

in human symptoms. ProSmartHealth uses the Support Vector Machine ranking algorithm to train and test

diagnostic suggestions. This work aims to study the application’s reliability, using two strategies. First, study

the inﬂuence of pre-processing data analysing the impact in the accuracy method when data is previously pro-

cessed. The second strategy aims to study the inﬂuence of the number of training data on the method precision.

This study concludes the use of pre-processing data and the number of training data inﬂuence the precision of

the model, improving the precision on 8%.

1 INTRODUCTION

Through the availability of data, the variety of data

analysis techniques and the information processing

capacity of computers, Artiﬁcial Intelligence, Ma-

chine Learning and Deep Learning have increasingly

contributed to the medical industry in different do-

mains and applications, enabling the creation of pre-

dictive models that allow the study of transmis-

sion and identiﬁcation of disease risk, among others

(Academy, 2015), (Jiang et al., 2017).

This study consisted of developing an applica-

tion that provides diagnostic suggestions to support

health professionals in order to reduce diagnostic er-

rors. This application manages, for now, diagnoses of

three types of diseases: breast cancer, dementia and

heart disease.

The application, called ProSmartHealth, uses Su-

pervised Learning approach (Kim, 2017), through

classiﬁcation strategy to identify the type of diag-

noses. ProSmartHealth uses Support Vector Machine

method with algorithms of linear binary classiﬁcation

and multi-class approach.

Although ProSmartHealth obtained satisfactory

results, achieving a precision of 84%, there were

many aspects to improve. So, the application update

was done using two strategies: the inﬂuence of the

https://orcid.org/0000-0003-4995-4799

https://orcid.org/0000-0003-3803-2043

pre-processing data on the train set and the perfect

number of training data to obtain the best model ac-

curacy.

The paper is organised as follows: The section

2 presents a review of the literature on how Artiﬁ-

cial Intelligence, Machine Learning and Support Vec-

tor Machine can be applied in the health ﬁeld. The

3 section demonstrates the ProSmartHealth applica-

tion. The 4 section presents the problem to be devel-

oped and the 5 section presents a description of the

databases used. The numerical results will be pre-

sented and analysed in Section 6. Finally, Section 7

summarises the work with some conclusions and per-

spectives for future work.

2 LITERATURE REVIEW

Artiﬁcial Intelligence (AI) assists in several areas,

having already been implemented in several applica-

tions, namely in the autonomous car, on the factory

ﬂoor, in the hospital service system, in social net-

works, on the mobile phone, among others (Russel

and Norvig, 2004). The AI system can support doc-

tors by providing up-to-date medical information, in

addition, with a large number of information, it is pos-

sible to create software that warns of risk, or can di-

agnose and predict the onset of diseases (Miotto et al.,

2017).

Sena, I. and Pereira, A.

Trends Identiﬁcation in Medical Care.

DOI: 10.5220/0010384502950302

In Proceedings of the 10th International Conference on Operations Research and Enterprise Systems (ICORES 2021), pages 295-302

ISBN: 978-989-758-485-5

295

Machine Learning (ML) is used more often in

the health area, performing segmentation of medi-

cal images, named image registrations, image fusion,

computer-assisted diagnosis, image-guided therapy,

image annotation, and data set retrieval image (Khare

et al., 2017). An example of use is the development of

a model for a hospital classiﬁcation based on a diag-

nosis of hospitalisations, with and without emergency,

where it is predict the urgency of admissions with a

numerical value that reﬂects the degree of planning

available for a hospital. This study helps to identify

the increasing emergency services in hospitals, which

can be a complex scheduling problem (Kr

amer et al.,

2019).

Support Vector Machine (SVM) was already ap-

plied in healthcare by increasing diagnostic accuracy.

An example of use on the prediction of Alzheimer’s

disease, which has the ﬁnal diagnosis provided with

the corresponding values. This training is carried out

through a binary classiﬁcation method. In this study,

the algorithm was able to predict dementia and vali-

date its performance through statistical analysis (Bat-

tineni et al., 2019).

3 ProSmartHealth APPLICATION

The ProSmartHealth application consists in an intelli-

gent system to identify clusters and patterns in human

symptoms and aims to assist health professionals in

the diagnosis process, through an integrated question-

naire with key questions that the professional ﬁlls in

with patient data and obtains a diagnostic suggestion

(for now, three diseases are available: heart disease,

breast cancer and dementia).

This application was developed, in the Matlab

software, combining three model codes that provide

a diagnostic suggestion for each disease individually.

The application procedure started by studying which

is the best algorithm for the data set to be used through

Matlab Classiﬁcation Learner application, obtaining

the best results from the Support Vector Machine.

To use this classiﬁcation method, it is necessary

that the data set is divided into two sets: training and

testing. The training data set has 85% of the total nu-

merical data and the test data set has the remaining

15% of the numerical data. These data sets were used

to train and test the model in 100 iterations with the

appropriate algorithms for each data set, taking into

account that the Support Vector Machine algorithms

depend on the number of classes to be identiﬁed.

For breast cancer and heart disease, have two

classes, the ﬁtcsvm function is used with the mini-

mum sequential optimisation algorithm, the function

implements the algorithm represented in Figure 1.

Figure 1: Linearly Separable Problem algorithm (N. Deng

and Zhang, 2013).

As dementia data has three classes, the ﬁtcecoc func-

tion is used, which executes the “one vs one” method

with a linear kernel, the algorithm implemented by

the function can be seen in Figure 2.

Figure 2: One vs One method algorithm (N. Deng and

Zhang, 2013).

The three models codes were combined to cre-

ate the ProSmartHealth application. The application

starts with a home page where the health professional

can choose the disease to acquire a diagnostic sugges-

tion, as shown in Figure 3.

Depending on the disease chosen by the health

professional, a window will appear with a question-

naire that will have a number of questions representa-

tive of the parameters used to train and test the model,

in the case of breast cancer there are ten, for demen-

tia there are 5 and for heart disease there are thirteen

parameters, which are explained in Section 5.

When the health professional ﬁnishes ﬁlling out

the chosen questionnaire the system will send a mes-

sage indicating a diagnostic suggestion.

ICORES 2021 - 10th International Conference on Operations Research and Enterprise Systems

296

Figure 3: ProSmartHealth application home.

4 PROBLEM STATEMENT

Diagnostic errors are increasingly common in the

health area, which is beginning to worry the popu-

lation (dos Santos et al., 2010). These errors can oc-

cur in different sectors and for several reasons, one of

them is the fact that hospitals are often overcrowded

and without enough doctors to monitor 100% of all

patients.

Therefore, the ProSmartHealth application aims

to reduce diagnostic errors, assisting health profes-

sionals.

Given this, this study is based on the study of the

reliability of ProSmartHealth through two strategies.

The ﬁrst strategy consists of studying the impact of

pre-processing data. The second strategy is based on

studying the inﬂuence of the number of training data

on the accuracy of the model.

5 DATA SET

CHARACTERISATION

For the application, it was necessary a data set in

which each data is formed by a set of input parameters

(characteristics) and a set of classes, which represent

the phenomenon of interest on which it is intended to

make predictions. So far, the application considers the

diagnosis associated with heart disease, breast cancer

and dementia.

This study used three different data sets, one for

each disease. The data set for breast cancer contains

569 data in 10 parameters were obtained from the

University of Wisconsin through an aspiration biopsy

(Qassim, 2018), dementia includes 150 data in 5 pa-

rameters acquired through the series of open access

imaging studies (Battineni et al., 2019), and heart dis-

ease covers 303 data in 14 parameters obtained by the

Cleveland data set (Latha and Jeeva, 2019).

Next, the different parameters of each disease that

characterise each database will be presented.

5.1 Breast Cancer

The data set consists of 357 patients who were diag-

nosed with a benign nodule and 212 patients were di-

agnosed with a malignant nodule. The ten input pa-

rameters and the used in this study were:

Radius: Calculated by averaging the distance from

the center to the perimeter points. This parameter

comprises values between 6.98 mm and 28.11 mm;

Texture: Calculated using the standard deviation of

the gray scale values. The values vary between 9.71

and 39.3;

Perimeter: Calculated through the distance from the

center to the perimeter points, diameter (d). The vari-

ation of values happens between 43.79 mm and 188.5

mm;

Area: Calculated using 2π × d. This parameter com-

prises values between 143.5 mm

and 2501 mm

;

Smoothness: Local variation in radius lengths. The

variation in the values of this parameter is included in

0.0526 and 1158;

Compression: It is calculated using

perimeter

/area − 1.0. The values vary between

0.0107 and 0.3;

Concavity: Severity of the concave portions of the

contour. The range of values for this parameter is be-

tween 0 and 0.5;

Concave: Measurement of the surface deeper in the

center than at the end. This parameter comprises val-

ues between 0 and 0.27;

Symmetry: Symmetry of the duct holes. This param-

eter comprises values between 0 and 0.3;

Fractal Dimension: Value of ”approaching the

coast”. The range of values for this parameter is be-

tween 0 and 1.2;

Diagnosis: This database obtains two possible diag-

noses, benign and malignant nodules, represented by

0 and 1, respectively.

5.2 Dementia

This data set comprises results from 150 different pa-

tients, where 77 of the patients are characterised as

non-demented, 58 of the individuals as demented, and

the remaining 15 as converts, that is, non-demented at

the time of their initial visit and subsequently char-

acterised as demented, on a later visit. The six input

parameters taken into account were as follows:

Trends Identiﬁcation in Medical Care

297

Age: It is the main risk factor for diseases of the de-

mentia level. This parameter includes ages from 60 to

93 years old;

Clinical Dementia Rating (CDR): It is a tool for

studying dementia that classiﬁes individuals with dis-

abilities in each of the seven domains: memory,

guidance, judgement and problem solving, function

in community affairs, home, hobbies and personal

care. Based on the collateral source and the patient

interview, a score is obtained if CDR = 0 without

Alzheimer’s, if CDR = 0.5 Very mild Alzheimer’s,

CDR = 1 Mild Alzheimer’s, CDR = 2 Moderate

Alzheimer’s;

Mini-Mental State Examination (MMSE): It is a

neuropsychological test that evaluates, from 0 to 30,

several abilities, such as reading, writing, orientation

and short-term memory. Thus, a score greater than 24

points indicates healthy cognitive performance, be-

tween 19 and 23 points corresponds to mild dementia,

between 10 and 18 points represents moderate demen-

tia and less than 9 points represents severe dementia;

MR Delay: It is the interval between each image re-

moved; This parameter comprises values that vary be-

tween 0 and 2639;

Normalized Whole-Brain Volume (n-WBV): is cal-

culated using an image that is initially segmented to

classify brain tissue as cerebral spinal ﬂuid, gray or

white matter. The segmentation procedure iteratively

assigned voxels (matrix of volume elements that con-

stitute a three-dimensional space) to tissue classes.

The n-WBV is then calculated as the amount of all

voxels within each fabric class. This parameter com-

prises values that vary between 0.6 and 0.8;

Diagnosis: This database obtains three possible diag-

noses, non-demented, converted and demented, rep-

resented by 0, 1 and 2, respectively.

5.3 Heart Disease

The data set used in the Cleveland database consists

of a longitudinal collection of 303 patients, where 138

of the patients have no heart disease, and 165 of the

patients have heart disease. The fourteen input pa-

rameters used in this study were:

Age: The most important risk factor in the develop-

ment of heart disease, because blood pressure tends to

increase with age, this is due to the fact that blood ves-

sels have lost their elasticity. This parameter includes

ages from 29 to 77 years old;

Gender: Men are at higher risk for heart disease than

women in pre-menopause, but after menopause the

risk is similar to that of a man. In the database, 0

corresponds to women and 1 to men;

Angina (Chest Pain): Angina is chest pain or dis-

comfort caused when the heart muscle does not re-

ceive enough oxygen-rich blood. The type of chest

pain that the patient feels is displayed by: 1 (typical

angina), 2 (atypical angina), 3 (non-angina pain) and

4 (asymptotic);

Resting Blood Pressure: One of the risk factors, be-

cause high blood pressure can damage the arteries

that feed the heart, and the increase in blood pres-

sure inside the arteries also causes the heart to have to

greater effort to pump blood. The value is displayed

in mmHg, where the values for systolic (maximum)

vary between 120 mmHg and 180 mmHg and for di-

astolic (minimum) vary between 80 mmHg and 110

mmHg;

Cholesterol: Because a high level of low-density

lipoprotein (LDL) cholesterol is a risk factor for car-

diovascular disease. The value is represented in

mg/dl, which varies between 130 mg/dl and 50 mg/dl,

meaning very high cardiovascular risk and low car-

diovascular risk, respectively;

Fasting Blood Sugar: Not producing enough hor-

mone secreted by the pancreas (insulin) or not re-

sponding properly to insulin causes your body’s blood

sugar levels to rise, increasing the risk of a heart at-

tack. An individual’s fasting blood sugar value is

compared with 120 mg/dl, if the value is greater than

120 mg/dl, then it has a value of 1 (true) otherwise a

value of 0 (false);

ECG at Rest: Because it is an exam that detects the

electrical activity of the heart, being the most used

to assess cardiac arrhythmia’s. Displays results such

as: 0 (normal), 1 (with ST-T wave abnormality) and 2

(left ventricular hypertrophy);

Maximum Heart Rate Reached: increased cardio-

vascular risk is associated with accelerated heart rate.

The values vary between 60 bpm and 100 bpm, which

represent, low heart rate and high heart rate, respec-

tively;

Exercise-induced Angina: The pain or discomfort

associated with angina can vary from mild to severe,

with an afﬁrmative value of 1 and negative if 0;

Exercise ST Segment Peak: An exercise test on the

ECG is considered abnormal when there is a horizon-

tal ST depression or downward slope ≥ 1 mm at 60-

80 ms after the J point. The parameter comprises val-

ues that vary between 0 and 6.2 mm;

Peak Exercise ST Segment: The duration of ST seg-

ment depression is also important, as prolonged re-

covery after peak stress is consistent with a positive

stress test on the ECG, can be represented in case of

climb by 1, in case of stability / plane by 2 and in case

of descent by 3;

Number of Main Vessels: From 0 to 4 which are col-

ICORES 2021 - 10th International Conference on Operations Research and Enterprise Systems

298

ored by ﬂuoroscope, is displayed by an integer value;

Thalassemia: It is a group of inherited diseases

resulting from an imbalance in the production of

one of the four chains of amino acids that make up

hemoglobin, in 3 (normal), 6 (corrected defect) and 7

(reversible defect);

Diagnoses: This database obtains two possible diag-

noses, absence and present, represented by 0 and 1,

respectively.

6 RESULTS ANALYSIS

As already mentioned, the reliability of the applica-

tion was studied using two strategies: the ﬁrst strategy

is to measure the impact of the pre-processing data,

and the second is based on the study of the inﬂuence

of the number of training data on the accuracy of the

response.

In the ﬁrst strategy, the data set is pre-processed,

by cleaning the data set, removing all outliers found.

Then the precision of the model is calculated for the

data set with pre-processing, through the same proce-

dure, being compared with the precision obtained for

the original data set.

The second strategy is based on studying the in-

ﬂuence of the number of training data on the model’s

accuracy. In which the accuracy of the model with

10k cross-validation will be calculated for three dif-

ferent training sets, one with 85% (performed in the

previous strategy), another with 75% and ﬁnally 65%.

The results are obtained for both data sets, with and

without pre-processing of the data, to verify whether

these factors matter for the calculation of the model’s

precision.

It is necessary to bear in mind that the test data

set is always the same for all attempts so that the re-

sults are reliable. In the following section, these two

strategies were carried out for each disease studied,

analysing each result, referring at the end to the im-

provements that this study brought to the application.

6.1 Breast Cancer

As mentioned in Section 5.1, the original breast can-

cer data set consists of 569 data, of which 357 belong

to the benign class and 212 to the malignant class.

For the ﬁrst strategy, the boxplot technique was

used to remove all outliers from the database, in

which 107 outliers of the benign class and 40 outliers

of the malignant class were identiﬁed and removed,

so the treated data set contains 422 data.

Subsequently, the prediction of the model was cal-

culated using the data set treated using the procedure

referred to in Section 3, where the set was divided into

a training set with 85% of the numerical data (359)

and a set of test with 15% of numerical data (63).

Then the precision obtained for the original and

treated data set was compared, which can be seen in

Table 1.

Table 1: Comparison of precision values.

Original data set Treated data set

Precision 84.71% 98.41%

By analysing Table 1, it can be indicated that the

model’s accuracy improves when the data set is pre-

processed, even if only the outliers have been re-

moved.

In order to observe the improvements in the preci-

sion of the model, the confusion matrices were com-

pared for each data set, analysing whether the percent-

age of false negatives and positives decreases with the

use of the treated data set. Figure 4 shows the confu-

sion matrices for each data set.

Figure 4: Comparison of confusion matrix.

Through Figure 4 it is possible to calculate the per-

centage of false negatives and positives for each set of

data used. For the original data set, 56.47% true pos-

itives, 28.24% true negatives, 9.41% false negatives

and 5.88% false positives were obtained. And, for the

treated data set, 57.14% true positives, 41.27% true

negatives, 1.59% false positives and 0.00% false neg-

atives were obtained. These results indicate that when

using a treated data set it is possible to obtain better

results, in this case it is clear that with the cleaning of

the data set, no false positive diagnosis is obtained.

Then, the second strategy was performed, in

which the model precision was calculated for each

data set with 75% and 65% of the data in a 10k cross-

validation, the test data are the same as those used

in the ﬁrst strategy (15%) for each data set. Table 2

intends to compare the model’s precision values ob-

tained.

Trends Identiﬁcation in Medical Care

299

Table 2: Precision comparison with different numbers of

training data.

Training percentage 75% 65%

Original Data set 86.71% 85.88%

Pre-processed Data set 96.99% 96.51%

When checking the precision results of both Ta-

bles, 1 and 2, one can compare whether the precision

value decreases from 85% to 65% for each data set.

Then it can be indicated that through the treated data

set this happens, which is usually correct, because the

smaller the number of data to be used, the precision

should decrease because there is less data to train the

model.

6.2 Dementia

As mentioned in Section 5.2, the dementia data set

consists of 150 data, and through the boxplot tech-

nique it was observed that there were 9 outliers of the

non-demented class, 5 of the converted and 29 of the

demented, thus the treated data set contains 107 data.

To calculate the accuracy of the model with the

treated data set, it was divided into two sets: training

set with 91 numerical data (85%) and test set with 16

numerical data (15%).

Then, in Table 3, it is possible to observe the com-

parison of precision between the original data set and

the treated one.

Table 3: Comparison of precision values.

Original data set Treated data set

Precision 86.36% 93.75%

By analysing the Table 3 it is possible to indicate

that when performing the pre-processing of the data,

even if it is only the removal of the outliers, the accu-

racy of the model increases.

In order to better observe the differences between

the obtained precision, the confusion matrix was cre-

ated for each data set. Their comparison can be seen

in Figure 5.

Through the confusion matrices observed in Fig-

ure 5 it is possible to calculate various percentages

and check if there have been improvements in them

for each data set. For the original data set 68.18% true

positives, 18.18% true negatives and 6.82% false neg-

atives and 6.82% false positives were obtained, and

for the treated data set, the following percentages of

results are obtained: 81.25% true positives, 6.25%

true negatives and 6.25% false negatives and 6.25%

false positives.

With these results it can be indicated that there is

an improvement in the results when using the treated

data set, however, there is a lower percentage of true

Figure 5: Comparison of confusion matrix.

negatives that can be justiﬁed through the use of fewer

test data representative of the demented class, because

there are fewer test data set and data are chosen at

random.

Then, the second strategy is performed, which

consists of calculating the model precision for each

data set with a different number of training data in a

10k cross-validation, although the test data for each

set are the same as those used in the ﬁrst strategy. Ta-

ble 4 shows the comparison of these results.

Table 4: Precision comparison with different numbers of

training data.

Training percentage 75% 65%

Original Data set 86.36% 86.36%

Pre-processed Data set 91.25% 87.50%

Using Table 3 and 4, it can be indicated that for the

treated data set, the precision decreases as the number

of data used to train the model decreases, the same is

not the case for the original data set. This may indi-

cate that the treated data set is more reliable.

6.3 Heart Disease

As mentioned in the 5.3 section, the original heart dis-

ease data set consists of 303 data, and in the ﬁrst strat-

egy, the data were pre-processed by removing out-

liers, where 47 outliers were found. absence class

and 36 of the presence class, the data set being treated

with 220 data.

To calculate the accuracy of the model with the

treated data set, it is necessary to divide it into two

sets: training and testing. The training set contains

85% of the numerical data (187) and the test set con-

tains the remaining 15% of the numerical data (33).

It should be noted that the precision of the model was

calculated using the same process referred to in Sec-

tion 3 for the original data set.

ICORES 2021 - 10th International Conference on Operations Research and Enterprise Systems

300

Table 5: Comparison of precision values.

Original data set Treated data set

Precision 82.22% 84.85%

Through Table 7 it is possible to observe that with

the use of the treated data set the precision of the

model increased. However, to verify this increase,

a confusion matrix was created and compared with

the one calculated for the original data set. Figure 6

shows the two confusion matrices.

Figure 6: Comparison of confusion matrix.

With Figure 6 it is possible to calculate various

percentages that demonstrate the reason for the in-

crease in precision between the treated and original

data set. 57.78% true negatives, 24.44% true posi-

tives, 11.11% false positives and 6.67% false nega-

tives were obtained for the original data set, and for

the treated data set, if 60.60% true negatives, 24.24%

true positives, 9.09% false negatives and 6.06% false

positives. Analysing these results, it can be indicated

that there was an improvement in the results when us-

ing a treated data set.

Then the second strategy was carried out, which

aims to study the inﬂuence of the number of training

data on the calculation of accuracy. Accuracy is cal-

culated for two more cases, 75% and 65% of the train-

ing data, maintaining the test sets used in the previous

strategy for each data set. Table 6 presents the com-

parison of the precision results of each data set for

each case.

Table 6: Precision comparison with different numbers of

training data.

Training percentage 75% 65%

Original Data set 82.00% 82.66%

Pre-processed Data set 84.55% 83.34%

When analysing the Table 4 and 6, it can be seen

that for the data set treated, the precision of the model

decreases as the percentage of numerical data used to

train the model decreases, which does not happen for

the original data set. This strategy demonstrates that

both the number of training data to be used and the

pre-processing of data inﬂuences the accuracy of the

model.

Table 7 shows the comparison between the orig-

inal and treated data set for the results obtained for

the ProSmartHealth application. Where is indicated

the total precision obtained by the application (Prec.),

The percentage of true positives (TP), true negatives

(TN), false negatives (FN) and false positives (FP).

Table 7: Precision comparison of ProSmartHealth applica-

tion.

Data Set Prec. TP TN FN FP

Original 84.00% 49.70% 34.73% 7.63% 7.94%

Treated 92.34% 54.21% 36.04% 4.63% 5.11%

Through Table 7 it is possible to observe that there

was an evolution in the results with the use of the

treated data sets. Where the accuracy of the model

has increased and the percentage of data predicted in-

correctly has decreased.

7 CONCLUSIONS AND FUTURE

WORK

Support Vector Machine is a good solution for the

medical industry, supporting numerous sectors in the

area, and in this case, by diagnosing patients early, it

is possible to support healthcare professionals to re-

duce errors in diagnosis, reducing the hospital stress.

It can be concluded that the pre-processing and

the number of training data inﬂuence the model ac-

curacy. There is an improvement in the results in the

three databases, where breast cancer data achieves the

best results, obtaining a very pleasant model precision

and containing minimum percentages of false posi-

tives and negatives.

In general, it is concluded that using a data set

with data pre-processing improves the precision of the

application by 8% in relation to the data sets without

pre-processing. Therefore, it can be stated, through

this study, that the pre-processing of the data and the

number of training data that is used to train the model

inﬂuence its accuracy.

Considering all this reliability analysis, the ProS-

martHealth application was updated with databases

with pre-processing, changing to ProSmartHealth 2.0,

an improved application with better results precision.

However, there are still many aspects to improve, such

as being applied to a greater number of diseases, and

Trends Identiﬁcation in Medical Care

301

further decreasing the likelihood of obtaining false di-

agnoses.

Referring that the ProSmartHealth 2.0 application

was not developed with the intention of replacing

health professionals, but rather helping them, with

some alerts.

In the future, the ProSmartHealth 2.0 application

may be applied to a set of data with symptoms that

covers a greater number of diseases, which will allow

a suggestion of diagnosis with the percentage of con-

tracts each of these diseases that can be observed with

the data entered in the questionnaire.

ACKNOWLEDGEMENTS

This work has been supported by FCT — Fundac¸

para a Ci

encia e Tecnologia within the Project Scope:

UIDB/05757/2020.

REFERENCES

Academy, D. S. (2015). Deep learning book.

http://deeplearningbook.com.br/uma-breve-historia-

das-redes-neurais-artiﬁciais/. Online; accessed: 01

April 2020.

Battineni, G., Chintalapudi, N., and Amenta, F. (2019).

Machine learning in medicine: Performance calcu-

lation of dementia prediction by support vector ma-

chine (svm). Informatics in Medicine Unlocked,

(16):100–200.

dos Santos, M. C., Grilo, A., Andrade, G., Guimar

aes, T.,

and Gomes, A. (2010). Comunicac¸

ao em sa

ude e a

seguranc¸a do doente: problemas e desaﬁo. Revista

Portuguesa de sa

ude p

ublica, 10:43–57.

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang,

Y., Dong, Q., Shen, H., and Wang, Y. (2017). Arti-

ﬁcial intelligence in healthcare: past, present and fu-

ture. Stroke and Vascular Neurology, 27(2):97–111.

Khare, A., Jeon, M., Sethi, I. K., and Xu, B. (2017). Ma-

chine learning theory and applications for healthcare.

Journal of Healthcare Engineering, page 2.

Kim, P. (2017). Matlab, Deep Learning – With Machine

Learning, Neural Networks and Artiﬁcial Intelligence.

Apress, London, 2nd edition.

amer, J., Schrey

ogg, J., and Bussel, R. (2019). Classiﬁca-

tion of hospital admissions into emergency and elec-

tive care: a machine learning approach. Health Care

Manag Sci, (22):85–105.

Latha, C. B. C. and Jeeva, S. C. (2019). Improving the

accuracy of prediction of heart disease risk based on

ensemble classiﬁcarion techniques. Informatics in

Medicine Unlocked, (16):1–9.

Miotto, R., Wang, F., Wang, S., Jiang, X., and Dudley, J. T.

(2017). Deep learning for healthcare: Review, oppor-

tunities and challenges. Brieﬁngs in Bioinformatics,

6(19):1236–1246.

N. Deng, Y. T. and Zhang, C. (2013). Support Vector Ma-

chine - Optimization Based Theory, Algorithms, and

Extensions. Chapman & Hall Book/CRC Press, Min-

neapolis.

Qassim, A. (2018). Breast cancer cell type classi-

ﬁer. https://towardsdatascience.com/breast-cancer-

cell-type-classiﬁer-ace4e82f9a79. Online; accessed:

03 May 2020.

Russel, S. and Norvig, P. (2004). Intelig

encia Artiﬁcial: Un

Enfoque Moderno. Pearson Educaci

on, S.A, London,

2nd edition.

ICORES 2021 - 10th International Conference on Operations Research and Enterprise Systems

302