=Paper=
{{Paper
|id=Vol-2353/paper12
|storemode=property
|title=Modification of the Genetic Method for Neuroevolution Synthesis of Neural Network Models for Medical Diagnosis
|pdfUrl=https://ceur-ws.org/Vol-2353/paper12.pdf
|volume=Vol-2353
|authors=Serhii Leoshchenko,Andrii Oliinyk,Sergey Subbotin,Nataliia Gorobii,Vadym Shkarupylo
|dblpUrl=https://dblp.org/rec/conf/cmis/LeoshchenkoOSGS19
}}
==Modification of the Genetic Method for Neuroevolution Synthesis of Neural Network Models for Medical Diagnosis==
https://ceur-ws.org/Vol-2353/paper12.pdf
Modification of the genetic method for neuroevolution
synthesis of neural network models for medical diagnosis
Serhii Leoshchenko1[0000-0001-5099-5518], Andrii Oliinyk2[0000-0002-6740-6078] ,
Sergey Subbotin3[0000-0001-5814-8268], Nataliia Gorobii4[0000-0003-2505-928X] and
Vadym Shkarupylo5[0000-0002-0523-8910]
1,2,3,4
Dept. of Software Tools, Zaporizhzhia National Technical University, Zaporizhzhia
69063, Ukraine
5
Dept. of Computer Systems and Networks, National University of Life and Environmental
Sciences of Ukraine, Kyiv 03041, Ukraine
sergleo.zntu@gmail.com, olejnikaa@gmail.com,
subbotin@zntu.edu.ua, gorobiy.natalya@gmail.com,
shkarupylo.vadym@nubip.edu.ua
Abstract. The main aim of the paper is researching the possibility of applica-
tion artificial neural networks as the neural network models that can be used in
medical diagnostics. One of the most problematic and complex issues of neural
network models implementation is the initial stage of synthesis. The article pre-
sents a comparison of existing methods of synthesis, as well as a new method.
The experiments confirm the effectiveness and expediency of the proposed
method.
Keywords: artificial neural networks, synthesis, neuroevolution, genetic meth-
od, support vector machine.
1 Introduction
The diagnosis stage plays a crucial role in medicine. Timely accurate diagnosis facili-
tates the choice of therapy and significantly increases the probability of treatment of
the patient. The using of neural networks is one of the ways to improve the efficiency
of medical diagnosis [1].
The accuracy of the diagnosis and the speed with which it can be delivered depend
on many factors: the patient's condition, the available data on the symptoms and signs
of the disease and the results of laboratory tests, the total amount of medical informa-
tion on the observation of such symptoms in a variety of diseases and, finally, the
qualification of the doctor. A major role in this process is played by the human factor,
which often leads to errors [1], [2].
Some of the specific medical diagnosis difficulties that need to be considered are
listed below [3].
The basis for a reliable diagnosis is a wealth of practical experience, which can be
reached only the middle of a doctor's career and is absent at the end of training, of
course. This is especially true for rare or new diseases, where experienced doctors are
in the same situation as beginners.
The quality of diagnosis depends on the skill, knowledge and intuition of the doc-
tor.
Emotional problems and fatigue adversely affect the work of the doctor.
Training of specialists is a long and expensive procedure, and therefore in many,
even in developed countries, there is a lack of skills.
Medicine is one of the fastest growing and developing fields of science. New re-
sults disqualify the old ones, new drugs appear every day. The same applies to the
diseases themselves, which take new forms.
These factors necessitate the search for new solutions and tools, for example, the
use of artificial neural networks (ANNs) [2].
2 Using the ANN in medical diagnosis
The ANN technologies are designed to solve difficult-to-formalize problems, which,
in particular, are reduced to many problems of medicine [4], [5]. This is primarily due
to the fact that the researcher is often provided with a large number of heterogeneous
factual material for which a mathematical model has not yet been created. In addition,
it is necessary to present the results of the analysis in a form, which will be under-
standable to the specialist. So ANN is a powerful and flexible method of simulating
processes and phenomena. Neural networks can be different in structure and form, but
they have several common features. A distinctive feature of neural networks is their
ability to training on the basis of experimental data of the subject area. With regard to
medical subjects, experimental data are presented in the form of a set of initial fea-
tures or parameters of the object and the diagnosis based on them. Training of ANN is
an interactive process in which the neural network finds hidden nonlinear relation-
ships between the initial parameters and the final diagnosis, as well as the optimal
combination of weight coefficients of neurons connecting adjacent layers, in which
the classification error tends to a minimum [6]. In the training process, the input of the
neural network is fed a sequence of initial parameters along with the diagnoses that
characterize these parameters. Careful formation of the training sample determines the
quality of work, as well as the level of error of the neural network.
A number of difficulties are associated with the use of neural networks in practical
problems. One of the main problems of application ANN technologies is a previously
unknown degree of complexity of the projected ANN, which will be enough for a
reliable diagnosis. This complexity can be unacceptably high and will require more
complex network architecture. It is known, for example, that the simplest single-layer
neural networks are able to solve only linearly separated problems [7]. This limitation
can be overcome by using multilayer neural networks.
The basis of ANNs are neurons with a structure similar to biological analogues.
Each neuron can be represented as a microprocessor with several inputs and one out-
put. When neurons are joined together, a structure is formed, which calls a neural
network. Vertically aligned neurons form layers: input, hidden and output. The num-
ber of layers determines the complexity and, at the same time, the functionality of the
network, which is not fully investigated.
For researchers, the first stage of creating a network is the most difficult task. The
following recommendations are given in the literature [8–10].
1. The number of neurons in the hidden layer is determined empirically, but in most
cases the rule is used N h N i N o , where N h is the number of neurons in the
hidden layer, N i in the input and N o output layers.
2. Increasing the number of inputs and outputs of the network leads to the need to in-
crease the number of neurons in the hidden layer.
3. For the ANNs modeling multistage processes required additional hidden layer, but,
on the other hand, the addition of hidden layers may lead to overwriting and the
wrong decision at the output of the network.
Based on these recommendations, the number of layers and the number of neurons in
the hidden layers is chosen by the researcher, based on his personal experience.
3 Review of the literature
The ANN are attractive from an intuitive point of view, because they are based on a
primitive biological model of nervous systems. In this connection, there is an assump-
tion that to improve it may be appropriate to apply another borrowing from nature, for
example, evolutionary calculations and, in particular, neuroevolution. Neuroevolution
in this paper refers to the automatic modification of neural networks using genetic
algorithms. With this methodology, possible variations of neural networks with dif-
ferent topologies are grown, which with each iteration, called generation solve the
problem better. Despite genetic programming, as well as evolutionary calculations in
general, do not guarantee finding the optimal result, this approach eventually allows
us to come to the results applicable to solving practical problems. However, it will
take a reasonable amount of time to achieve such results. Thus, the level of complex-
ity of the neural network that arises when it is necessary to create a neural network is
significantly reduced, because when it is created, it is only necessary to select the
parameter that evaluates the work of the neural network and provide a suitable set of
data.
Despite the fact that most of the works devoted to the neuroevolutionary approach
offer only a theoretical approach to solving problems of neural network optimization,
it is possible to find several promising and noteworthy methods [11–14].
From the early works of noteworthy cellular Frederick Gruau method [15], [16] us-
es a special grammar for the representation of neural network structures. One individ-
ual represented an entire neural network, with each neuron considered as a biological
cell, and the growth of the network was determined through the mechanisms of se-
quential and parallel "division" of neurons - cells. However, this method involves the
implementation of a large number of specific operators that provide simulation of cell
activity.
The Hierarchical SANE (Symbiotic, Adaptive NeuroEvolution) [17] method uses
a different approach. It is consider the development of two independent populations,
one of which individuals are separate neurons, and the other contains information
about the structures of an artificial neural network. The disadvantages of this method
include the fact that the number of hidden neurons and connections is limited.
The ESP method [18] is a development of the sane method. Its main difference is
that the network structure is fixed and is given a priori. The population of neurons is
divided into subpopulations, in each of which the evolution is independent. Due to
parallelization of the solution search, as well as simplification of the problem due to
the rejection of the evolution of the artificial neural network structure, ESP works
much faster than SANE, sometimes by an order of magnitude, but for the successful
operation of the method it is required to choose the appropriate structure of the neural
network [19].
One of the most potentially successful attempts to get rid of the disadvantages of
direct coding while preserving all its advantages is the method proposed in 2002,
called NEAT — Neural Evolution through Augmenting Topologies [15], [20]. De-
signed by Kenneth Stanley, the NEAT method allows to customize the structure of
the network, and without restrictions on its complexity. The solution proposed by the
authors is based on the biological concept of homologous genes (alleles), as well as
on the existence in nature of the synapsis process — the alignment of homologous
genes before the crossover. The technique assumes that two genes (in two different
individuals) are homologous if they are the result of the same mutation in the past. In
other words, with each structural mutation (gene addition), a new gene is assigned a
unique number, which then does not change during evolution. The method uses a
number of techniques, such as historical labels and specialization of individuals, to
make the process of evolution significantly more efficient [21].
Summing up, it can be noted that the joint use of evolutionary methods and artifi-
cial neural networks allows us to solve the problems of configuration and training of
artificial neural networks both individually and simultaneously. One of the advantages
of this synthesized approach is largely a unified approach to solving a variety of prob-
lems of classification, approximation, control and modeling. The use of qualitative
evaluation of the functioning of artificial neural networks allows the use of neuroevo-
lutionary methods to solve the problems of the study of adaptive behavior of intelli-
gent agents, the search for game strategies, signal processing. Despite the fact that the
number of problems and open questions concerning the development and application
of neuroevolutionary methods (coding methods, genetic operators, methods of analy-
sis, etc.) is large, often for the successful solution of the problem with the use of neu-
roevolutionary method adequate understanding of the problem and neuroevolutionary
approach, as evidenced by a large number of interesting and successful works in this
direction [15].
4 Materials and methods
In the method, which is proposed to find a solution using a population of neural net-
works: P NN 1 , NN 2 ,..., NN n , that is, each individual is a separate ANN
Ind i NN i [19–21]. During initialization population divided into two halves, the
genes g Indi g1 , g 2 ,..., g n of the first half of the individuals is randomly assigned
g Indi g1 Rand, g 2 Rand,..., g n Rand . Genes of the second half of the popu-
lation are defined as the inversion of genes of the first half
g Ind i g 1 Rand , g 2 Rand ,..., g n Rand . This allows for a uniform distribution
of single and zero bits in the population to minimize the probability of early conver-
gence of the method ( p min ).
After initialization, all individuals have coded networks in their genes with-out
hidden neurons (Nh), and all input neurons (Ni) are connected to each output neuron
(No). That is, at first, all the presented ANNs differ only in the weights of the in-
terneuron connection wi. In the process of evaluation, based on the genetic informa-
tion of the individual under consideration, a neural network is first built, and then its
performance is checked, which determines the fitness function ( f fitness ) of the indi-
vidual. After evaluation, all individuals are sorted in order of reduced fitness, and a
more successful half of the sorted population is allowed to cross, with the best indi-
vidual immediately moving to the next generation. In the process of reproduction,
each individual is crossed with a randomly selected individual from among those
selected for crossing. The resulting two descend-ants are added to the new generation
G P ` Ind 1 , Ind 2 ,..., Ind n . Once a new generation is formed the mutation operator
starts working. However, it is important to note that the selection of the truncation
significantly reduces the diversity within the population, leading to an early conver-
gence of the algorithm, so the probability of mutation is chosen to be rather large
( p mut 15 - 25% ) [22].
If the best individual in the population does not change for a certain number of
generations (by default, it is proposed to set this number at eight), this individual is
forcibly removed, and a new best individual is randomly selected from the queue.
This makes it possible to realize the exit from the areas of local minima due to the
relief of the objective function, as well as a large degree of convergence of individuals
in one generation. The general scheme of the method demonstrated at Fig.1.
4.1 Using of genetic operators
It is obvious that the chosen method requires special genetic operators that implement
crossover and mutation.
At crossover two parental individuals which produce two descendants are used.
Common neurons and connections are inherited by both offspring, and the value of
connections in the networks of descendants are formed by a two-point crossover.
Elements of ANN, of distinct played out between generations.
An important feature is that neurons with the same indices are considered identical,
despite the different number of connections and position in the network, as well as the
fact that one of these neurons could have a different index, which changed as a result
of correction of indices after mutation. For this purpose, three coefficients were intro-
duced that regulate the size and direction of the network.
The first of them characterizes the degree of connectedness of neurons in the net-
work and is calculated by the formula:
Nc (1)
f con
2 FB 1N s N s 1 Ni Ni 1 1 FB No N o 1
where N c is the number of connections in the network, N i , No , N s are respec-
tively, the number of input, output neurons and the total number of neurons in the
network, FB is a variable indicating the permitted occurrence of feedbacks ( FB =1)
or not ( FB =0). It is worth noting that connections from hidden neurons to the output
can appear in any case. Thus, the smaller f con the more likely a new relationship will
be added as a result of the mutation [23].
P NN 1 , NN 2 ,..., NN n
g Ind g 1 , g 2 ,..., g n
i
w i
Ind i
G P ` Ind 1 , Ind 2 ,..., Ind n
Fig. 1. The general scheme of the method
The use of the second coefficient is based on the assumption that the more elements in
the sum of the input and output vectors of the training choice (the greater the total
number of input and output neurons), which is probably a more complex network is
necessary to solve the problem. The second coefficient is calculated by the formula:
Ni N o
ftop.diff (2)
Ns
That is, the more neurons in the network, the less will be f top .diff and the less like-
ly will be selected mutation that adds a new hidden neuron [23].
The third criterion is also based on the assumption that a more complex network
should be used to solve more complex problems. However, this criterion characterizes
the conditional complexity of the network. This criterion is based on the concept of
cyclomatic complexity [24], [25].
Ni N o
f comp;.diff (3)
Ns
For any of the described cases, the algorithm uses a ligament
f con f top .diff f comp .diff , because for use it is necessary to take into account the de-
gree of connectivity of already existing neurons.
Thus, using mutations can be pointwise to change the parameters of the structure of
the ins.
Chaotic the addition (removal) of neurons and connections can lead to situations
where, for example, in a network of many neurons and few connections. It would be
more logical to apply different types of mutations depending on the features of the
network architecture represented by the mutating individual [26–28].
Removing links gives a side effect: there may be hanging neurons that have no in-
coming connections, as well as dead-end neurons, that is, without output connections
[26], [27], [29]. In cases where the function of neuronal activation is such that at zero
weighted sum of inputs its value is not equal to zero, the presence of hanging neurons
makes it possible to adjust the neural displacement. It is worth noting that, on the
other hand, the removal of links may contribute to the removal of some uninformative
and uninformative input features.
4.2 Choosing the mutation type
Consider the dependence of the type of mutation on the values f con f
, top.diff
and f comp .diff . Adaptive mutation mechanism is one of the key features of the pro-
posed method.
The choice of mutation type is determined based on the values of f con f
, top.diff
and f comp .diff . This approach, on the one hand, does not limit the number of hidden
neurons from above, on the other hand, it prevents the immeasurable increase of the
network, because the addition of each new neuron to the network will be less likely.
The mutation of the weight of a random existing bond occurs for all mutating indi-
viduals with a probability of 0.5.
Let us consider in more detail how to choose the type of mutation. Fig. 2 shows the
block diagram of the selection of the type of mutation. Here RV is a random variable,
Nh is the number of hidden neurons in the mutating network.
Conventionally, the entire algorithm can be divided into two branches on the first
conditional transition:
─ branch increase f c is carried out for the fulfilment of the conditions of transition;
─ branch reduction f c , performed if the transition condition is not met.
Fig. 2. The diagram of the selection of the type of mutation
Multiplication f con f comp .diff is necessary in order to change the number of neurons
adequately network topology, because the addition (removal) of neurons need infor-
mation about the feasibility of changes. This information can be obtained indirectly
from the value of the characteristic.
4.3 The calculation of the output layer of ANN
On condition using the support vector machine, the optimality criterion for calculating
the output weights may not be specified. If the value of the mean square error is re-
placed by the criterion of the maximum separation of the support vectors, then the
optimal linear weights of the output can be estimated using, for example, quadratic
programming, as in the traditional method of support vectors, for this it is advisable to
use the Evoke operator [30], by the formula:
k li
y t w0 wi , j K t , i j , (4)
i 1 j 0
where t R is the output of a recurrent neural network f at a time t ; K , is
n
a predefined kernel function; wi, j is weights corresponding to k training sequences
i , each length l i , and are calculated using the support vector machine.
The value of the mean square error is replaced by the criterion of maximum separa-
tion of support vectors. In this case, the optimal linear weights can be estimated using
quadratic programming, as in the traditional support vector machine.
One of the problems of neuroevolutionary method realization is the algorithm of
ANN output calculation with arbitrary topology.
ANN can be represented as a directed planar graph. Based on the fact that the net-
work structure can be any, loops and cycles containing any nodes are allowed in the
graph, except for the nodes of the corresponding input neurons. Let denote the set of
nodes of the graph by V vi | i 0; N v 1 , and a set of arcs through
E e j | j 0; N e 1 , where N v and N e are accordingly, the number of nodes and
arcs in the graph, and Nv N s , and Ne Nc . The arc, which goes from node k to
node 1 denote by an ordered pair c k , l v k , vl , the weight of the corresponding link
will be denoted by wk ,l .
Give the index to the nodes of the graph as neurons, that is, the nodes that are the
input neurons, called input, have an index out of range 0; N l 1 . By analogy, the
indexes of outgoing nodes belong to the interval N l ; N l N o 1 , and indexes for
hidden nodes will be set in the interval N l N o ; N v 1 .
Let introduce an additional characteristic for all nodes of the graph equal to the
minimum length of the chain to any of the input nodes and denote it chi . Let's call
chi the layer to which the ith node belongs. Thus, all input nodes belong to the 0th
layer, not all input nodes that have input arcs from the input belong to the 1st layer, all
other nodes with input arcs from nodes of the 1st layer will belong to the layer with
index 2, etc .in this case, there may be situations when the node does not have input
arcs, we will call it a hanging node with the layer number chi 1 .
For arcs, we also introduce an additional characteristic bk ,l for the arc c k ,l , which
is necessary to determine whether the arc corresponds to forward or reverse. It will be
calculated as follows:
1, chl chk 0
bk ,l (5)
1, chl chk 0
That is, if the index of the layer of the end node of the arc is greater than the index
of the layer of the beginning node, then we will consider such an arc as a straight line,
otherwise we will consider the arc as an inverse.
Since each node of the graph represents a neuron, we denote by sumi the value of
the weighted sum of inputs, and through oi is the value of the output (the value of the
activation function of the ith neuron-node). Then, oi f fitness sumi where f fitness is
the function of neuron activation.
Let's divide the whole process of signal propagation from the input nodes into
stages, and during one such stage the signals manage to pass only one arc. The num-
ber of the stage is denoted by s. For the very first stage s=1. For short assumed that all
arcs have the same length, and the signals are sewn on them instantly. We denote the
feature that the output of node i was updated at this stage through ai , that is, if ai 1,
then the output of the node at stage s is calculated, otherwise, if ai 1 – not.
Let's introduce one more designation X xi | i 0; N l 1 it is vector of input
signals. Then the algorithm for calculating the ANN output is as follows:
1. oi x i , ai 1 , for all i 0; N l 1 ;
2. oi 0 , for all i N l ; N s 1 ;
3. s=1;
4. sumi 0 , ai 1 , for all i N l ; N s 1 ;
5. if s 1, than go to the step number 7;
6. calcultion of the feedback network. For all input feedbacks c j ,k node v k , where
k N l ; N s 1 : sumk sumk o j , if ch j s ;
7. if a i 0 , than fn (i ) for all i N l ; N s 1 ;
8. if the stop criterion is not met, than s=s+1 and go to the step number 4.
Fig. 3. The general scheme of the calculation of the output layer of ANN
Here fn (i ) is a recursive function that calculates the output of the 1st node taking
into account all straight arcs. Works on the following algorithm:
1. if chi 0 , than go to the step number 3;
2. for all input arcs c k ,l node vi : if ak 1 , than sum i sum i o k , else fn(k ) ;
3. oi f sumi ;
4. exit.
The stopping criterion of the ANN output calculation algorithm can be one of the
following:
─ stabilization of values at the output of ANN;
─ s exceeds the set value.
It is more reliable to calculate the output until the values at the output of ANN do not
change, but for the case when the network contains cycles and/or loops, its output
may never become stable. Therefore, the required additional stopping criteria limiting
the maximum number of stages of calculation of network output. For networks with
no feedback ( FB =0) in many cases, allow the max chi 1 phases.
5 Experiments
During testing, the main task is to track the speed of the proposed method, quality and
stability. Since synthesized ANN can be further used as diagnostic models for medical
diagnosis, testing should be carried out on the relevant test data. Also, testing will be
carried out in 2 stages: the first stage will consist in the synthesis of ANN only with
the help of modified genetic algorithm, and the second – in additional processing of
the initial layer by the support vector machine. This strategy will allow to know more
clearly how useful the support vector machine is.
Data for testing were taken from the open repository – UC Irvine Machine Learn-
ing Repository. Data sample was used: Breast Cancer Coimbra Data Set [31]. Clinical
features were observed or measured for 64 patients with breast cancer and 52 healthy
controls. There are 10 predictors, all quantitative, and a binary dependent variable,
indicating the presence or absence of breast cancer. The predictors are anthropometric
data and parameters which can be gathered in routine blood analysis. Prediction mod-
els based on these predictors, if accurate, can potentially be used as a biomarker of
breast cancer. Table 1 shows the main characteristics of the data sample.
Table 1. Main characteristics of the Breast Cancer Coimbra Data Set
Criterion Characteristic Criterion Characteristic
Data Set Characteristics Multivariate Number of Instances 116
Attribute Characteristics Integer Number of Attributes 10
During the evaluation of the test results we will pay attention to the following criteria:
─ the spent time, s;
─ average error of final network ( E );
─ standard deviation (SD).
The relative error value in this case will be calculated as the ratio of the classifica-
tion error to the total sample size (number of instances).
errorclass
E 100% , (6)
Numbersampl
where E is relative error; errorclass is classification error; Numbersampl the number
of instances in the sample.
Standard deviation gives an idea about how one or the other, the ANN accurately
predicts the user's rating, since the estimation is calculated the difference between the
result of the work of ANN’s and known result. It is also important to know that this
indicator can be calculated only with a sufficient amount of observations. Otherwise,
the calculation of the SD will be uninformative and its use will not lead to improve-
ment of the results of the ANN.
Numbersampl 2
1
SD xi x , (7)
Numbersampl i 1
where SD is standard deviation, xi is ith element of the set, Numbersampl the number
of instances in the sample, x is the mean value of these observations [32].
6 The results analysis
Table 2 shows the results of testing the modified genetic algorithm in comparison
with the NEAT and ESP methods.
Table 2. Results of testing
Time, s E SD
NEAT 468.013 4.40% 3.70
ESP 9389.55 3.07% 2.99
Modified GA 631.373 2.96% 3.05
As the table shows, the modified GA according to the time of fulfillment is ahead of
ESP in time, however, inferior to the NEAT. However, it should be noted that in the
analysis of error values, the proposed method is significantly ahead of existing meth-
ods.
Let's repeat testing, but now with additional use of the support vector machine. The
results are shown in Table 3.
Table 3. Results of testing
Time, s E SD
NEAT 468.013 4.40% 3.70
ESP 9389.55 3.07% 2.99
Modified GA (with support
5326.326 2.36% 2.17
vector machines)
As can be seen from the table, the modified GA using the support vector machine is
inferior to the opponents in terms of execution time. However, on indicators of mini-
mum, maximum errors and average errors of the output ANN significantly better than
their competitors. Therefore, we can conclude that the use of the support vector ma-
chine really significantly improves the results of the synthesis.
Fig. 4. The distribution of iterations in experiments
As you can see from the diagram, the modified genetic method was more iterative
than the existing methods, but the time spent on iteration was less. That is, it can be
concluded that iterations are not complex and for their reduction it is possible to resort
to parallelization, which will significantly speed up the work even when using the sup-
port vector machine [33–35].
7 Conclusion
The problem of finding the optimal method of synthesis of ANN requires a compre-
hensive approach. Existing methods of ANNs training are well tested, but they have a
number of nuances and disadvantages. The paper proposes a mechanism for the use a
modified genetic algorithm for its subsequent application in the synthesis of ANNs.
Based on the analysis of the experimental results, it can be argued about the good
work of the proposed method. However, to reduce iterativity and improve accuracy, it
should be continued to work towards parallelization of calculations.
Acknowledgment
The work was performed as part of the project “Methods and means of decision-
making for data processing in intellectual recognition systems” (number of state regis-
tration 0117U003920) of Zaporizhzhia National Technical University.
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