=Paper=
{{Paper
|id=Vol-2917/paper48
|storemode=property
|title=Bayesian Methods Application for the Differential Diagnosis of the Chronic Obstructive Pulmonary Disease
|pdfUrl=https://ceur-ws.org/Vol-2917/paper48.pdf
|volume=Vol-2917
|authors=Volodymyr Lytvynenko,Mariia Voronenko,Olena Kovalchuk,Ulzhalgas Zhunissova,Luidmyla Lytvynenko
|dblpUrl=https://dblp.org/rec/conf/momlet/LytvynenkoVKZL21
}}
==Bayesian Methods Application for the Differential Diagnosis of the Chronic Obstructive Pulmonary Disease==
https://ceur-ws.org/Vol-2917/paper48.pdf
Bayesian Methods Application for the Differential Diagnosis of
the Chronic Obstructive Pulmonary Disease
Volodymyr I. Lytvynenko1, Mariia O. Voronenko1, Olena Kovalchuk2,
Ulzhalgas Zhunissova3 and Luidmyla N. Lytvynenko4
1
Kherson National Technical University, Berislavske Shosse, 24, Kherson, 73008, Ukraine
2
National Pirogov Memorial Medical University, st. Pirogova, 56, Vinnytsya, 21018, Ukraine
3
Astana Medical University, st. Beibitshilik 49A, Astana, 010000, Kazakhstan
4
Kherson city neuropsychiatric center, st. Morska, 1, Kherson, 73003, Ukraine
Abstract
The paper proposes a methodology for attachment Bayesian models in the differential
diagnosis of a disease such as chronic obstructive pulmonary disease in different age groups
patients with the obligatory presence of 1, 2, or three concomitant diseases in anamnesis. The
ways for building the Bayesian models structure are considered. The medical experts,
pharmacists, specialists were screening of input data for creation the probabilistic predicting
system.
Keywords 1
Diagnostic Methods, Accompanying Illnesses, Bayesian Networks, Feature Selection
methods, Clustering, The algorithm MeanShift
1. Introduction
The medical diagnostics main task is to define possible diagnoses based on examination data. The
main difficulties faced by doctors at the medical diagnosis stage are:
the amount of present illnesses is greater than the amount of ways for their diagnosis, which
rises the misdiagnosis risk;
the diagnosis ambiguity, i.e. the presence of comorbidities that strongly distort the symptoms
picture;
a large indicators number, which also leads to data distortion;
lack of individual approach to each patient [1].
The medical diagnosis peculiarity is that a qualified doctor for a certain group of patients can
clearly diagnose any disease, but each of the patients may have not one disease, but several at the
same time. That is, the patient may have several diagnoses (the doctor may prescribe for each
diagnosis a subjective level of belonging).
There may also be situations where the physician is unable to diagnose a patient's illness due to a
lack of experience or due to atypical or distorted patient symptoms. From the computational
intelligence standpoint, this situation is the subject of active learning [2], when the diagnostic system
in the process of its training processes both various observations, which know exactly the value of the
training signal (reference signal) and a priori unclassified observations (self-learning mode). The
chronic obstructive pulmonary illness is the reason of death among children and the elderly today.
MoMLeT+DS 2021: 3rd International Workshop on Modern Machine Learning Technologies and Data Science, June 5, 2021, Lviv-Shatsk,
Ukraine
EMAIL: immun56@gmail.com (V. Lytvynenko); mary_voronenko@i.ua (M. Voronenko); elena.kovalchuk972@gmail.com (O.
Kovalchuk); Ulzhalgaszhunisova@gmail.com (U. Zhunissova); llytvynenko58@gmail.com (L.Lytvynenko)
ORCID: 0000-0002-1536-5542 (V. Lytvynenko); 0000−0002−5392−5125 (M. Voronenko); 0000-0002-3102-7973 (O. Kovalchuk); 0000-
0001-5255-9314 (U. Zhunissova); 0000-0001-8445-5704 (L.Lytvynenko)
©️ 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
The incidence of the chronic obstructive pulmonary disease in elderly patients increases dramatically
while they are being treated in hospital facilities for another concomitant disease.
Modeling and differential diagnosis, taking into account the presence of several comorbidities, are
quite difficult, since they are characterized by a complex structure of dependences. The computational
problems are also obvious since the analysis of a large database requires a long processing time and
can be performed only with adequate computational infrastructure. When resources are limited and
research is mainly based on open data, there is a need for models that can be modified depending on
the variability of functions, and thus can further use the experience to improve the predictability of the
model. Bayesian networks (BNs) are the most suitable computational tools for this.
The study is dedicated to the creation of a system that makes it possible to achieve the
effectiveness of drug therapy in the presence of the patient's main diagnosis and several concomitant
diagnoses that aggravate the course of the disease. The task of this work is to make a methodology for
constructing a BN in the differential diagnosis of a disease such as the chronic obstructive pulmonary
disease.
The main contribution is as follows:
(i) realization of a comprehensive causal probabilistic model for the differential diagnosis of three
types of pulmonary diseases,(ii) development of a specific methodology for planning the development
of Bayesian networks for differential diagnosis problems, (iii) application of the MeanShift cluster
analysis algorithm to identify specific signs, symptoms, laboratory and instrumental findings
characteristic of the disease forms under study, and (iv) the use of algorithms to estimate the
information entropy, which made it possible to assess the informativeness of the features about the
corresponding class of the disease.
Thus, the main problem is related to improving the quality of differential diagnosis in medical
decision support systems. The key problem focuses on improving the quality of differential diagnosis
through the comprehensive use of methods for selecting the most informative features.
The article is drawn up like this. Section 2 defines the tasks that we will solve in our study. Section
3 shows the literature list on existing ways for the differential diagnosis. In Section 4 we discuss the
general formulation of the solution to the problem. Section 5 describes the input data and methods for
obtaining structural model indicators. After that, in the Section 6 we describe the sequence of building
and validating a BN. We present the research process and its results. Section 7 presents the study
results analysis. Section 8 summarizes and completes.
2. Problem Statement
Using Bayesian Approach the developed model will facilitate a faster diagnosis and thus help to
pre-select the correct treatment method. This mathematical tool let us to describe the notions, which
the patient uses when describing his health [3].
For a set of events X i , i 1,K , N that are related, and a set of learning data
1 2 N
D d1 ,K , d n , di xi xi K xi , is given. Here the subscript is the observation amount, and the
upper one is the variable amount, n –is the amount of surveillances, each surveillance comprises
N N 2 variables, and each j -th variable j 1,K , N has such conditions:
j
A 0 ,1,K , 1
j
2 .
j
Based on a given training sample, you need to build an acyclic graph connecting the event sets
X i , i=1,…,N . In addition, each BN structure g G is presented by a set N of predecessors
P ,K , P , that is, for each vertex j 1,K , N , P it is a variety of parent vertices, such that
1 N j
P X ,K , X \ X . Research will be carried out in accordance with the following stages of
j 1 N j
Bayesian network development (Figure 1).
The aim of this engineering is to create the Bayesian-based model for the early diagnosis of the
chronic obstructive pulmonary disease if the patient has the likelihood of concomitant diagnoses.
3. Review of the Literature
Many different fuzzy approaches to data clustering are currently used [4-6]. They are able to
effectively cluster data in situations where clusters overlap, assuming that the cluster size is small, i.e.
do not contain abnormal emissions. Real medical and biological data sets contain up to 20% of
emissions. The data of these indicators allow forming clusters that allow to estimate dynamics of
indicators, to simplify and accelerate the process of diagnostics.
Existing methods for estimating biological conditions are based on the values arrays analysis of
the measured parameters set given in the vectors form. These can be immunological, biochemical,
physiological, cytogenetic, cytomorphological, and other medical and biological data.
Figure 1: The stages according to which the research goal will be achieved.
The measurement data analysis of an individual condition of a separate organism consists in the
definition of belonging of this condition to any of in advance known conditions (diagnoses). Each
specialist in certain types of diseases makes "his" diagnosis. This is due to objective reasons related to
the overlap and coincidence of different numbers of indicators of the state of the body for different
diagnoses, and the widespread prevalence of so-called polysyndromic conditions.
Making correct and effective decisions in such situations requires a significant amount of time and
money to organize consultations of highly qualified persons. The most stringent in terms of fuzzy
clustering procedures are the so-called fuzzy clustering algorithms, based on objective functions [5, 7,
8] and made automatic classification (without teacher) by optimization predefined quality criteria. As
a result of this procedure, the formed clusters have the shape of hyperspheres, which significantly
limits the possibility of using the above methods to process data of more complex forms. In the
artificial intelligence field, the computer system that imitates the possibility to make human decisions
is called an expert system [9]. The first expert systems were created in the 1970s and became
widespread in the 1980s. They were one of the first truly successful artificial intelligence forms.
However, some experts note that expert systems were not part of true artificial intelligence, as they do
not have the ability to learn independently of external data [10].
The MYCIN system, for example, has been designed to recommend the required amount of
antibiotics depending on the patient's weight [11]. Studies at Stanford Medical School found that
MYCIN offered acceptable therapies in approximately 69% of cases, better than infectious disease
experts who were evaluated according to the same criteria.
However, when looking at the expert systems in real operating conditions, there are other
problems, such as integration, access to large databases, and performance [12]. The CADUCEUS
medical expert system was created to help diagnose blood infections as well as diagnose internal
diseases. CADUCEUS was able to recognize up to 1,000 different diseases, as well as to recognize
the several concomitant diseases presence. Fuzzy medical diagnosis in terms of increasing numbers
signs and diagnoses are represented by online diagnosis systems. To date, there are medical online
diagnostic systems DNFS and AWDNFN, designed on the basis of W-neuron. During their operation,
there will be a worse criterion for the effectiveness of AWDNFN than in DNFS due to the longer
processing time with better diagnostic accuracy [13].
In the past, many authors have tried to create computational intelligence systems for diagnosis
using data sets from the medical repository as input data [14-18]. The inability to process data in real
time, a fixed number of medical signs, and a low convergence rate are the disadvantages of these
systems. In [19] was proposed the use of probabilistic methods of medical diagnosis, which raised
expert systems to a new level of development.
4. Materials and Methods
4.1. Data
The study involved 137 patients of different ages, different anamnesis, and varying disease
severity. The data set describes the results of clinical tests of patients, symptoms, indicators of
preliminary examination and complaints with which the patient consulted a doctor. All patients were
given a preliminary diagnosis of the chronic obstructive pulmonary disease, and each patient has two
more concomitant diagnoses that can complicate the course of the infection and the patient's
treatment. Our primary task is to identify the presence of concomitant diagnoses and predict scenarios
in which these diagnoses may complicate the course of the disease or aggravate the ongoing medical
therapy. In total, the data set contains 104 parameters that describe the condition of each of 137
patients.
In situations where medical symptoms may include abnormal emissions, obstructions, and other
artifacts, robust online procedures should be used that allow for consistent self-learning diagnostics
using arbitrary cluster diagnoses. We carried out preprocessing of the data using the Feature Selection
methods of optimization, and after clustering according to the algorithm MeanShift, the key indicators
that have the greatest impact were identified (see Table 1). Among them, there are known to us input
indicators and unknown indicators, the probability of occurrence of which we need to determine.
Table 1
The Input data
Designation of the node Description Interaction scheme
9 Suffocation
18 Increased body temperature
40 Harmful working conditions
47 Chest shape
50 Breathing is weakened
51 Wheezing rales
52 Wet wheezing
53 Dry wheezing
57 The volume of wheezing
89 Focus of inflammation
1 Diagnosis 1 yes / no?
2 Diagnosis 2 yes / no?
3 Diagnosis 3 yes / no?
The network contains ten nodes:
1. 9 corresponds to the patient's suffocation. This indicator has two states s0 and s1:
s0 means no suffocation from the patient,
s1 means the patient has suffocation.
2. 18 corresponds to the increased patient's body temperature. This indicator has two states s0
and s1:
s0 means the absence of information about increased patient's body temperature,
s1 means the presence of information about increased patient's body temperature.
3. 40 corresponds to the harmful working conditions in which the patient works. This indicator
has two states s0 and s1:
s0 means the absence of the harmful working,
s1 means the presence of the harmful working.
4. 47 is the patient's chest shape. This indicator has two states s0 and s1:
s0 refers to the barrel-like shape of the patient's chest;
s1 refers to the patient's round chest.
5. 50 is the patient's breathing is weakened/not weakened. This indicator has two states s0 and
s1:
s0 means that the patient's breathing isn`t weakened;
s1 means that the patient's breathing is weakened.
6. 51 corresponds to the wheezing/no wheezing in the patient's lungs. This indicator has two
states s0 and s1:
s0 means no wheezing in the patient's lungs,
s1 means wheezing in the patient's lungs.
7. 52 means presence /absence wet wheezing in the patient's lungs. This indicator has two
states s0 and s1:
s0 means the absence of wet wheezing in the patient's lungs,
s1 means the presence of wet wheezing in the patient's lungs.
8. 53 means the presence/absence of dry wheezing in the patient's lungs. This indicator has two
states s0 and s1:
s0 means the absence of dry wheezing in the patient's lungs,
s1 means the presence of dry wheezing in the patient's lungs.
9. 57 corresponds to the examination results of the patient's wheezing loudness. This indicator
has two states s0 and s1:
s0 means low volume of wheezing;
s1 means high volume wheezing.
10. 89 corresponds to the information whether there are foci of inflammation. This indicator has
two states s0 and s1:
s0 means that there are no foci of inflammation;
s1 means that there are foci of inflammation.
4.2. Future Selection
Modern data arrays, to which certain Data Mining methods can be applied, can be described by a
large amount of data that form a large-dimensional feature space. Therefore, the proportion of such a
space reducing to a dimension that allows data processing and/or visualization without unnecessary
difficulties is very urgent. The solution to such a problem is called the optimization of the feature
space or the search for significant features (Feature Selection, or Feature Engineering).
Data preprocessing is the most important stage, the quality of which determines the possibility of
obtaining high-quality results of the entire data analysis process. Feature Selection for the task being
implemented consists of choosing the most informative, useful features and excluding uninformative
features from consideration without transforming the original data space [20, 21]. We used the ID3
(Iterative Dichotomizer) algorithm proposed by D. Quinlan, which determines the order of a variable
and its attributes through their informational significance (informational entropy) [22]. To do this,
find the entropy of all unused features and their attributes relative to test specimens and choose the
one for which the entropy is minimal (and the information content is maximal). The entropy under the
condition of not equiprobable events pi is found by the well-known Shannon formula:
I pi log2 pi ,
i
(1)
where I is the amount of information in bits that can be transmitted using m elements in the message
with n letters in the alphabet, and pi=m/n.
4.3. Clustering algorithm MeanShift
MeanShift is a nonparametric modus for determining the location of the probability density
maximum [23]. The mean shift algorithm basically assigns data points to clusters iteratively, shifting
the points towards the highest data points density, that is, the centroid of the cluster. The Rosenblatt –
Parsen estimate is one of the most widely used for nonparametric data density estimation [24].
The density is estimated as the total influence of the sample elements, while the contribution of
each element is described by the bell-shaped function K(x), which depends on the distance to this
element. The formula for calculating the density estimate f(x) with the smoothing parameter
(bandwidth) P at an arbitrary point x has the form:
1 N x xi
fˆ x K
NP d i 1 P . (2)
As K(x), we can use the classical Gaussian kernel:
x xi x xi
KG exp 2
P 2P . (3)
However, in practice, in order to reduce computational costs, limited kernels are used, such as, for
example, the Epanechnikov kernel:
x xi x xi
K Ep
1
2
I x xi P2
P P , (4)
where I(x) is the indicator function.
In this approach, clusters correspond to local maxima of the density estimation function (modes).
And the data elements refer to clusters using the MeanShift procedure) [25], converging along the
gradient to the corresponding local maximum. An iterative procedure, starting its work from a point,
sequentially moves to a shift point xk+1=m(xk) until convergence, where:
x K x x .
N
m x i 1 i i
(5)
K x x
N
i 1 i
The vector is called the "mean shift" vector and its direction coincides with the direction of the
maximum density growth at the point x. Clustering algorithms based on the use of the mean shift
procedure allow obtaining high-quality partitions, however, the main problem for using this approach
is the high computational complexity [26].
4.4. Bayesian network methods
Let G (V , Bi ) be a graph in which the ending V is a set of variables; Bi is non-reflexive binary
relation on [27]. Each variable v has a kit of parent variables c(v) V and a kit of all descendants
d (v) V . A set s(v) is a set of child variables for a variable v and s(v) is a subset of d (v) . Let's
also mark, that:
(a(v) V ) V (d (v) {v}) . (6)
That is, a(v) is a kit of propositional signs from the set V, excluding the variable v and its
descendants. The set of variables B, is the contexture of parameters defining the model. It constitution
i i
Q x | pa ( X ) P ( x i | pa (X i )) for each x amount from X and pa( X i ) from Pa( X i ) , where Pa( X i ) means
i i
the variable Xi parents set in G . Each sign Xi in graph G is proposed as an apex. If we have more than
one graph, then we use the notation to recognize the parents PaG ( X i ) in graph G [28]. The total
probability B of Bayesian model is specified by the formula:
PB ( X 1 ,..., X N ) i 1 PB (Xi | Pa(Xi )) .
N
(7)
The parametric learning procedure purpose is to discover the most likely θ variables that
interpret the data [29].
For the validation procedure, we chose the algorithm presented in [30]. The method of
maximum expectation EM is a procedure of iterations, which was created for solve optimization tasks
of some functionality, using an analytical search for the objective function extremes. This way is
divided into two steps. At the first step of "expectation" (E - expectation) on the basis of available
observations (patients) the expected values for each incomplete observation are calculated. After
receiving the filled data set, the basic statistical parameters are estimated. In the second stage,
"maximization" (M - maximization) maximizes the degree of compliance of the expected and actually
substituted data [31].
5. Experiments and Results
The Bayesian model was established via the GeNIe 2.4 Academic software environment. The
structural static Bayesian model is shown in Figure 2. The Bayesian formula is used in Bayesian
networks as an inference tool to find a solution. If the Bayesian network is used to recognize
(identify) objects, then many factors are replaced by factors or characteristics of a particular object.
Selecting a set of instantiated variables separately has its advantages and disadvantages. The
advantage of this representation is that it prevents looping when forming the output. If the output is
not selected separately, there is a risk that the messages will affect each other and the network will
become unstable.
Figure 2: The static Bayesian network structural model
The disadvantage of this representation is that computing costs increase. However, the advantages
are so great that the allocation of instantiated variables is completely justified. The final decision to
confirm the effect between pollution data and test results, as well as the appointment of treatment, is
made by the doctor. In Table 2, we present the results of modeling each specific case from a sample
of data. The table shows the predicting result of confirming or refuting an early diagnosis.
Table 2
The scenario analysis results
Condition Result Description of cases, of interest
50 will decrease by 11% (54 to 43) When choking is at its maximum, the likelihood of
Clinical 52 will increase by 3% (47 to 50) breathing faster will decrease by 11%. The risk of
case 1: 57 will increase by 3% (46 to 49) being diagnosed with 3 will decrease by 10%
9 max 1 will decrease by 3% (44 to 41)
3 will decrease by 10% (61 to 51)
Clinical 2 will decrease by 6% (43 to 37) When the body temperature has reached its
case 2: 3 will increase by 4% (61 to 65) maximum, the risk of a diagnosis of 2 will decrease
18 max by 6%, and a diagnosis of 3 will increase by 4%.
Clinical 2 will increase by 7% (43 to 50) In the presence of harmful working conditions, the
case 3: 1 will decrease by 5% (44 to 39) risk of diagnosis 2 will increase by 7%, and 1 will
40 max 3 will increase by 2% (61 to 63) decrease by 5%.
Clinical 1 will decrease by 5% (44 to 43) In the presence of impaired breathing, the risk of
case 4: 3 will increase by 4% (61 to 64) choking increases by 10%
50 min 9 will increase by 10% (45 to 64)
Clinical 1 will increase by 4% (44 to 48) If the patient's breathing is weakened as much as
case 5: 3 will decrease by 3% (61 to 58) possible, the risk of a diagnosis 1 increases by 4%.
50 max 9 will decrease by 9% (45 to 36)
2 will increase by 4% (43 to 47) If the level of wet wheezing decreases, the risk of
Clinical
3 will decrease by 6% (61 to 55) diagnosis 3 will decrease by 6%. In this case,
case 6:
9 will increase by 3% (45 to 48) choking and wheezing volume may increase by 3%
52 min
57 will increase by 3% (46 to 49)
Clinical 2 will decrease by 3% (43 to 40) In the presence of pronounced wet wheezing, the
case 7: 3 will increase by 6% (61 to 67) risk of the diagnosis of 3 increases by 6%
52 max
Clinical 1 will increase by 4% (44 to 48) If the level of wheezing is minimal, the risk of
case 8: 2 will decrease by 4% (39 to 43) diagnosis 2 will be reduced by 4%
51 min 3 will increase by 4% (61 to 65)
Clinical 1 will increase by 6% (44 to 50) If the level of dry wheezing is minimal, the risk of
case 9: 2 will decrease by 4% (43 to 39) diagnosis 1 increases by 6%
53 min 3 will increase by 2% (61 to 63)
1 will decrease by 6% (44 to 38) In the presence of pronounced dry wheezing, the
Clinical
2 will increase by 4% (43 to 47) risk of the diagnosis of 2 increases by 4%, while
case 10:
3 will decrease by 2% (61 to 59) the risk of making a diagnosis of 1 will decrease by
53 max
6%
Clinical 2 will increase by 5% (43 to 48) At the maximum volume of wheezing, the risk of making a
case 11: 3 will increase by 3% (61 to 64) diagnosis of 2 increases by 5%
57 max
6. Discussion
Now let's analyze clinical cases. The analysis results are shown in Table 2 and in Figures 3-6. To
illustrate case 1 from Table 2: When choking is at its maximum, the likelihood that breathing will be
faster will decrease by 11%. At the same time, the risk of making a diagnosis of 3 will decrease by
10%, as shown in the Figure 3.
70%
-10%
60%
-11% 61%
50% 54% 51%
40% 43%
30%
20%
10%
0%
X50 X50´ Y3 Y3´
Figure 3: The internal profitability index tends to the maximum
Case 2: Upon reaching the maximum level of body temperature, the risk of a diagnosis of 2 will
decrease by 6%, and a diagnosis of 3 will increase by 4%, as shown in the Figure 4.
+4%
70%
60% 65%
61%
50% -6%
40% 43%
37%
30%
20%
10%
0%
Y2 Y2´ Y3 Y3´
Figure 4: Clinical case 2
Cases 4,11 and 12: If the patient's professional activity takes place in the presence of the maximum
harmful working conditions, the risk of diagnosis 2 will increase by 7%, and 2 will decrease by
5%, as shown in the Figure 5.
+7%
50%
50% -5%
45%
43% 44%
40%
39%
35%
30%
25%
20%
15%
10%
5%
0%
Y2 Y2´ Y1 Y1´
Figure 5: Clinical cases 4,11, and 12
Results of the analysis of case 10 are shown in the Figure 6.
+4%
50%
-6%
45% 47%
44% 43%
40%
38%
35%
30%
25%
20%
15%
10%
5%
0%
Y1 Y1´ Y2 Y2´
Figure 6: Clinical case 10
7. Conclusion
The chronic obstructive pulmonary disease gives dangerous complications to various organs of a
sick person, that is, patients with hypertension, diabetes, and coronary heart disease are at risk of
increased adverse outcomes.
The incidence of the chronic obstructive pulmonary disease depends on many factors: the standard
of living, social and marital status, working conditions, contact with animals, travel, the presence of
bad habits, contact with sick people, individual characteristics of a person, the geographic prevalence
of a particular pathogen.
Our study received comments from physicians and community physicians regarding the
applicability of our probabilistic estimates of diagnosis as a working tool of daily practice. Our
Bayesian system has also been evaluated by non-medical independent experts for comments from a
patient perspective.
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