INTRODUCTION
Recent studies prove that the machine learning is one of the most powerful
mechanisms that are used for diagnoses of diverse diseases and other areas of
research. The Detection of Erythemato-Squamous Diseases (DESD) is a difficult
problem in dermatology as these diseases display more than 90% common features
for both clinical and histopathological features (Guvenir
et al., 1998; Ubeyli and Guler, 2005). In
this study, we propose a new hybrid mechanism to inbuilt two powerful mechanisms
called the ABC algorithm and FELM classifier for the DESD. In this work, ABC
algorithm is used for a feature selection and FELM is used for a classification.
Several researchers have been contributing many machine learning algorithms
in medical diagnoses especially, in the field of DESD which are based on the
techniques like fuzzy logic, Artificial Neural Networks (ANN), Support Vector
Machine (SVM), among others. The interface design of the automatic DESD is required
for the dermatologist to access the diseases accurately with less computational
time. All the machine learning algorithms discussed so far in the recent literature
are all time consuming and are less accurate. This study proposes a hybrid mechanism
to address both the issues, namely accuracy and time.
Identifying and extracting information through the pattern analysis of any
task through feature selection and extraction which requires the large amount
of datasets. Feature extraction normally reduces the dimensionality of the datasets
and it eliminates the irrelevant, ambiguous and redundant data. In turn it extracts
the most relevant data towards the features selection and it is formulated into
an n-dimensional feature vector.
Feature selection is a crucial preprocessing technique for effective data analysis
and it reduces the time complexity as well as it improves the accuracy of the
data analysis process. This study focuses a feature selection method for data
analysis based on ABC algorithm and that can be used in different knowledge
domains through wrapper and forward strategies. This algorithm is mainly used
for solving optimization problems and now-a-days it can also be used for feature
selection and extractions.
The learning speed of the feed-forward neural networks depends on (1) Best
gradient based learning algorithms are used and (2) All the parameters of the
problem are tuned iteratively with the help of the gradient algorithm. But,
in many times it is difficult to concentrate the above reasons, because it is
largely varying from problem to problem. In (Huang, 2006)
developed a new and powerful algorithm, called ELM which is a feed-forward,
single-layered, neural networks (SLFNs) which randomly selects the hidden nodes
and analytically computes the outputs of SLFNs. Furthermore, (Huang
et al., 2006) proved that, the speed of the ELM is very fast than
it is compared to other multi-layered feed-forward neural networks.
In this work, the fuzzy component is incorporated with the existing ELM algorithm
and the result mechanism is called Fuzzy based ELM (FELM). A typical two-stage
dimensionality reduction of ABC-FELM structure is given in Fig.
1.
The main aim of this work is to design a Graphical User Interface (GUI) and
it will be useful for the dermatologist to detect the ESDs accurately with high
accuracy and very less computational time.
DIFFERENTIAL DIAGNOSIS OF ESD AND ITS BACKGROUND WORK
The differential diagnoses of ESD, namely psoriasis, seboreic dermatitis, lichen
planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris are
difficult problems in dermatology. The problems in predicting the diseases shall
share for almost 90% common clinical features of erythema and scaling. These
diseases frequently appeared in white skin people especially in USA and UK and
it rarely appears in Asian countries.
|
| Fig. 1: |
A typical structure of FELM |
| Table 1: |
Data set used- 34 features and 6 diseases |
|
At raw hand, all the diseases look closer alike erythema and scaling but when
inspected more rigorously, some patients have the typical clinical features
of the disease at the predilection sites while another group has typical localizations.
Initially, the intensity of the erythma and scaling was calculated for every
patient with the help of 12 clinical features and then it is further analyzed
with the help of other 24 histopathological features. The clinical and histopathological
features are given in Table 1.
Most of the time, the patients can be diagnosed only with the help of the clinical
features and some exceptional cases biopsy is also required for further confirmation.
Dermatologist can also use histopathological features for final confirmation
of the diseases through skin samples. This also helps the dermatologist to identify
the patients who have some other diseases at the beginning stage. For example,
when we test lichen planus disease it may lead to the patient having melanin
at the beginning stage.
Background work: In recent days, machine learning is one of the most
important methodologies of diagnosis of diverse diseases and other areas of
research. The DESD is a tough problem in dermatology as these diseases display
common features with some minor differences and it consists of six diseases
which are psoriasis, lichen planus, seboreic dermatitis, pityriasis rubra pilaris,
chronic dermatitis and pityriasis rosea. These diseases share all clinical features
with iota of difference and also many histopathological features (Guvenir
et al., 1998; Ubeyli and Guler, 2005). Fuzzy
set theory is used as decision making process in many areas of uncertainty/ambiguity
involved in medical diagnosis problems. These fuzzy sets have gained increasing
interest and attention in this modern world of information and technology, pattern
recognition, data analysis, production technique, diagnostics, decision making,
etc. (Ubeyli and Guler, 2005). Though neural networks
and SVM plays an important role in machine learning and data analysis, some
challenging issues exits which are intensive human intervene, slow learning
speed, poor learning, scalability and so on.
Few researchers have been concentrating the differential diagnosis of ESD and
some of the important results are listed here. The major contribution is given
by Guvenir at the initial stages and Ubeyli in the later stages. The period
considered here is 1998 to till date. The following table explains all the works
related to the prediction of ESDs with the help of the machine learning algorithms.
In 1998, the voting algorithm was proposed by Guvenir
and Cakir (2010) and they used for diagnosing the ESDs in a better way.
Furthermore, the prediction of ESDs has done by Guvenir
and Emeksiz (2000) with the help of three classifiers namely, nearest neighborhood
classifier, bayesian classifier and voting feature intervals. Castellano
et al. (2003) presented an application of a particular neuro-fuzzy
system, called KERNEL which has the ability to extract and refine knowledge
starting directly from observational data making use of neural learning. Nanni
(2006) presented an ensemble of Support Vector Machines (SVM) based on random
subspace and feature selection is developed. Each class has a ’favorite’
class and the best feature is calculated to discriminate the class. Ubeyli
(2008) presented a method based on the implementation of multiclass SVM
with error correcting output codes. An approach based on combined neural networks,
where the second level of the network uses output of the first level as input,
to improve accuracy in detection of the diseases was presented by Ubeyli
(2009). In the same year, Parthiban and Subramanian
(2009) described the determination of the intelligent agent for detection
of erythemato-squamous diseases by CANFIS and genetic algorithm. Again, Guvenir
and Cakir (2010) discussed k-means algorithm for the prediction of ESDs.
Davar et al. (2011) described the diagnosis
model based on catfish binary particle swarm optimization, kernelized support
vector machines and association rules as feature selection method to diagnose
ESD. An approach based on ensemble of data mining methods. However, these methods
have drawbacks like slow learning speed, more time consumption and need more
number of iterations as presented by Elsayad (2010)
and Xie et al. (2012) designed a new feature
selection procedure to find the data preprocessing and hence it leads to the
good diagnose of erythemato-squamous diseases. Again, Xie
et al. (2013) developed two-stage hybrid feature selection algorithm
for predicting ESDs . Aruna et al. (2012) developed
a hybrid feature selection method IGSBFS and it reduces the computational time
and gives high percentage of accuracy in terms of diagnose of diseases. Ravichandran
et al. (2013) discussed various machine learning algorithms including
ELM algorithm to diagnose ESD its performance which is high and the computational
time is reduced to less than 1 min. In this study no data preprocessing has
been discussed. The performance of accuracy is high whereas the time complexity
is very less when compared to all other machine learning algorithms.
Badrinath et al. (2013) proposed adaboost and
its hybrid algorithms for the detection of erythemato-squamous diseases. The
following steps have been used to design a GUI for automatic detection of ESDs:
(1) To find the feature selection for all the 34 parameters involved in the
ESD, (2) Association rules are then be used to find α% of transactions
(diseases) to meet β% of the features of the diseases, (3) To find the
support value of all the subset of α transaction from α% of transactions
and find the dominant subset through “Apriori Algorithm” and (4) Adaboost
and its hybrid classifiers are used to classify the diseases. In this case,
the percentage of accuracy is increased to 99.57% and the time complexity is
very high when compared to ELM.
This study aims to increase the percentage of accuracy and at the same time
the computational time will reduce to less than 1 min. The proposed algorithm
will address both the issues and its performance analysis with other machine
learning algorithms as given in Table 4.
The fuzzy set theory in generalization of Boolean algebra can be further explained
as process involving gradual transition that are used to classify classes, in
place of conventional crisp boundaries, the fuzzy values give accurate results
when given as input compared to normal input values. By combining fuzzy logic
and ELM, even some minor deviations, specified in the linguistic rules can be
smoothened during its input/output data training. When these proposed models
were evaluated and its performances were reported, significant improvement in
speed and accuracy compared to the previous models were achieved.
In this study, dermatology database consists of 366 patients’ reports
of ESD and was compiled by N. Ilter and H.A. Guvenir of Turkey (Ubeyli
and Guler, 2005; Ubeyli and Dogdu, 2010; Ubeyli,
2008, 2009). In this database 34 features of the
patients were recorded and this has been used for testing with the proposed
methods.
ABC algorithm: ABC algorithm was initially developed by Karaboga
(2005) and it is used for optimizing numeric related problems. This algorithm
is simple, robust and genetic based stochastic optimization algorithms. It simulates
the elegant and intelligent foraging behavior of honey bee swarms. The performance
of ABC was tested with some popular machine learning algorithms like genetic
algorithm, particle swarm optimization and differential evolution; and it is
proved that the performance of ABC algorithm is superior to other recent machine
learning algorithms (Basturk and Karaboga, 2006; Karaboga
and Basturk, 2007a, b). This algorithm contains three
groups of artificial bees which are employed bees, onlookers and scouts. Employed
bees are the bees which are already visited the food sources. Onlookers’
bees are the bees which are waiting on the dance area for finalizing the decision
of selecting the food source. The last kinds of bees are called scout bees which
discover the new food sources and this kind of the dancing bees to select the
random path to reach the new food sources. The random path is called the optimization
steps of the given optimization problem.
The position of food source is called the solution of the optimization problem
and the value of the nectar (amount of food source) is calculated through the
fitness formula and its procedure is given below.
In this procedure, first we have to eliminate less important or contribution
features before we apply the actual ABC algorithm:
| • |
Given the 12 clinical and 22 histopathological conventional
0-1 features as input of the problem. All these feature are then converted
into fuzzy based on Gaussian and Bell-shaped fuzzy membership function |
| • |
For removing the less contributed features, we have to convert the fuzzy
features into conventional 0-1 features with the help of the threshold value
Ω: |
If the degree of the feature is <Ω then (Eq. 1):
In this problem, we have considered the minimum threshold value Ω be 0.35.
This minimum threshold value is varying between the problems. The resultant
0-1 conventional vector of 34 clinical and histopathological features will be
sent to the input of the ABC algorithm. This process will reduce the dimensionality
considerably when compared to applying ABC algorithm directly (Eq.
2):
| • |
Let us assume that there are N numbers of
artificial bees taken as random and this is called size of the population.
The first N/2 bees are assumed to be the employed bees and the second N/2
bees are assumed as onlookers’ bees and hence, N/2 solution exists
in this problem. In this study, ABC algorithm generates a randomly distributed
initial population P (C = 0) of 234 solutions (position of the
food sources) and each solution is represented by a 34-dimensional vector.
Hence, the number of the employed bees is equal to the number of solutions
in the population. |
At the initial step, the food source positions are generated randomly and then
find the most appropriate population is obtained after repeatedly applying cycles
of the optimization search processes of all the three types of bees, namely
employed, onlooker and scout. The pseudo code for obtaining optimal population
is given Table 2.
Design of GUI for automatic DESD using ABC feature selection and FELM classifier:
All the experienced doctors in this field are not familiar to use high performance
computer in their professions. For that purpose, we need to construct a GUI
design that will accept all the features of the patient and then the proposed
system will predict the disease exactly by taking less computational time. It
is already proved that the computational efficiency that has shown ELM very
high when compared to all other technologies which was used in the recent literature.
Dermatology department in a hospital records the history of all the patients
which contains the information of test, results, histopathological information
and other details like family history, age and so on. Based on the test and
other results, doctors have difficulty to predict the erythemato-squamous disease.
The proposed mechanism will help the doctors to predict the disease exactly.
The design of GUI for the DESD is based on the machine learning algorithms,
namely ABC algorithm and FELM classifier. This is very much helpful for the
dermatologist to detect the diseases without biopsy and it can be predicted
with very less computational time. The flow diagram of the proposed GUI architecture
is given in Fig. 2.
| Table 2: |
Pseudo code representation of ABC algorithm |
|
|
| Fig. 2: |
A typical GUI architecture for the prediction of ESDs through
threshold based ABC-FELM algorithm |
The GUI consists of the following steps:
| • |
In the proposed GUI, the input dataset of the patients consists
of 34 features, out of which 12 are clinical features and 22 are histopathological
features |
| • |
Then, the dimensionality reduction is being done through ABC feature selection
algorithm. In this problem, the datasets consists of 34 features are finally
reduced to 22 features namely, ag, ac, aa, ae, f, l, o, t, y, v, g, h, u,
ad, i, x, j, ab, p, n, e and z |
| • |
From the reduced feature sets, we have to bifurcate into fuzzy and non-fuzzy
input variables. The fuzzy input variables are linguistically classified
into three levels, namely low, medium and high |
| • |
Fuzzy and non-fuzzy input variables are then fed into all the six ELM
classifiers and these six classifiers are used to classify all the six diseases
of the ESDs. That is, classifier-1 gives the fuzzified output value of the
disease-1 as far as the given input. Similarly, classifier-2 gives the fuzzified
output value of the disease-2 and so on |
| • |
Then, the output of the all the six classifiers are the inputs of the
7th classifier and its output will be called the significant ESDs of the
patients |
The threshold-based ABC-FELM is achieved the better dimensionality reduction
when compared to ABC-FELM algorithm.
From the available data sets (the reports of 366 patients was compiled by N.
Ilter and H.A. Guvenir of Turkey (Ubeyli and Guler, 2005;
Ubeyli and Dogdu, 2010; Ubeyli,
2008, 2009), ELM algorithm is trained with sufficiently
large number of training data set. It is trained till the error tolerance is
reached to 1e-06. In the trained system, test dataset is applied and the performance
is measured. The performance of the proposed classifier is recorded for both
data preprocessing stages, namely before and after data preprocessing. It is
available in Fig. 3 and 4.
|
| Fig. 3: |
Performance Analysis between the proposed (ABC-FELM) and other
machine learning algorithms |
|
| Fig. 4: |
Computational time (sec) before and after data preprocessing |
EXPERIMENTAL RESULTS AND DISCUSSION
Even an experienced dermatologist has difficulty in identifying the ESDs because
all those diseases have almost 90% of the unique features. In the technological
era, the high performance computational facility is available in many places
and with the help of its high computational efforts; one can easily determine
the diseases exactly. In this research, we have to address mainly two issues,
namely the accuracy and time. The recent literature survey shows that the accuracy
of the DESD is improved to 93-95% before data preprocessing and it is reached
to 96-99% after data preprocessing; where as in time, all the methods provides
high computational complexity. In the proposed methods it out performs for both
time as well as accuracy.
Out of 34 features listed in Table 1, ABC feature selection
algorithm finds that there are 21 features are very crucial to decide the disease
and other features are used as future references. The threshold based ABC-FELM
increased the percentage of accuracy when compared to not using Eq.
1.
Without using equation, ABC algorithm reduces the features from 34 to 22 and
following is the feature extraction. The dominant features are:
ag, ac, aa, ae, f, l, o, t, y, v, g, h, u, ad, i, x, j, ab,
p, n, e, z
and its accuracy is 99.26% after applying ABC-FELM algorithm. Again, we find
the extracted features after applying Eq. 1 called the ‘threshold-based
ABC Algorithm’ and we obtain the following 21 features which are:
ag, aa, ae, ac, f, l, t, o, v, g, y, h, u, ad, i, x, ab, p,
j, n, e
and its accuracy is raised from 99.26 to 99.57%. The advantage of the threshold-based
ABC algorithm is to remove all irrelevant and less important features before
applying ABC algorithm and it improves the percentage of accuracy. Clearly,
the given data set is reduced to 64.7% without using Eq.1 and using Eq.1 it
reduces to 61.7%. The performance analysis of before and after data processing
of fuzzy based ELM and other machine learning algorithms are given in Fig.
3 and 4. The processing time for ABC-FELM and threshold-based
ABC-FELM algorithm is same.
The entire dataset has been divided into training dataset (80%) and testing
dataset (20%). The disease classification of the proposed methodologies is given
in the form of the confusion matrix and it is represented in Table
3. The performance analysis of the proposed methodologies along with other
methods which are recently published is given in Table 4.
| Table 3: |
Confusion matrix |
|
| Table 4: |
Performance analysis of the proposed method and other machine
learning algorithm |
|
Table 3 shows that the overall accuracy of the proposed ABC
algorithm and fuzzy based ELM classifier after data preprocessing is 99.57%
approximately with Kappa value is 0.98996. For all the kappa calculation methods,
namely unweighted kappa, kappa with linear weight and kappa with quadratic weight,
the percentage of accuracy is obtained almost the same.
The performance analysis of ANN, ANFIS, ELM, SVM, AdaBoost Algorithms (Modest),
Hybrid-AdaBoost Algorithm (Adaboost and SVM) and the proposed algorithm is given
in Table 4.
Table 4 shows that Hybrid-AdaBoost algorithm performs better
than all other methods except threshold-based ABC-FELM and its accuracy is reached
to 99.26% but the processing times are very high. The percentage of accuracy
of the proposed threshold based ABC-FELM algorithm is 99.57% which is a little
bit higher than Hybrid adaboost algorithms but the processing time is very low,
that is less than 1 sec.
CONCLUSION
In this study, ABC-FELM algorithm was proposed for the detection of ESDs. The
data preprocessing has been done here by using ABC algorithm which has certainly
reduced the dimensionality of the data set and the time complexity. The fuzzy
logic played an important role and was used to work with the uncertainty in
differential diagnosis of ESDs which resulted in imprecise boundaries between
the six diseases. Existing methods such as neural networks, SVM, ANFIS and ELM,
AdaBoost algorithm, Hybrid Adaboost algorithm and the proposed ABC-FELM algorithm
were discussed in the study. They were useful for result comparison and detailed
analysis of the problem. The detailed analysis of the ABC-FELM algorithm produced
some conclusions associated with the affect of 34 features in the detection
of ESDs. The total classification accuracy of the threshold-based ABC-FELM algorithm
was reached to 99.57%. Hence, to conclude we state that the proposed ABC-FELM
algorithm can be of efficient use in the detection of ESD by considering speed
and accuracy compared to other models.
Subscribe Today
