https://doi.org/10.31449/inf.v45i3.3223 Informatica 45 (2021) 381–392 381
Extreme Learning Machines with Feature Selection Using GA for Effective
Prediction of Fetal Heart Disease: A Novel Approach
Debjani Panda
KIIT University, Bhubaneswar, India
E-mail: pandad@indianoil.in
Divyajyoti Panda
National Institute of Technology, Rourkela, India
E-mail: pandadivya02@gmail.com
Satya Ranjan Dash
KIIT University, Bhubaneswar, India
E-mail: sdashfca@kiit.ac.in
Shantipriya Parida (corresponding author)
Idiap Research Institute, Martigny, Switzerland
E-mail: shantipriya.parida@idiap.ch
Keywords: extreme learning machine, ga, feature selection, linear regression, ridge, lasso, heart disease
Received: July 1, 2020
Heart disease is considered to be the most life-threatening ailment in the entire world and has been a
major concern of developing countries. Heart disease also affects the fetus, which can be detected by
cardiotocography tests conducted on the mother during her pregnancy. This paper analyses the presence of
heart disease in the foetus by optimizing the Extreme Learning Machine with a novel activation function
(roots). The accuracy of predicting the heart condition of the foetus is measured and compared with
other activation functions like sigmoid, Fourier, tan hyperbolic, and a user-defined function, called “roots”.
The best features from the Cardiotocography data set are selected by applying the Genetic Algorithm
(GA). ELM with activation functions sigmoid, Fourier, tan hyperbolic, and roots (a novel function), have
been measured and compared on accuracy, sensitivity, specificity, precision, F-score, area under the curve
(AUC), and computation time metrics. The GA uses three types of regression: linear, lasso, and ridge, for
cross-validation of the features. ELM with user-defined activation function shows comparable performance
with sigmoid and hyperbolic tangent functions. Features selected from linear and lasso produce better
results in ELM than those selected from the ridge. It gives an accuracy of 96.45% as compared to 94.56%
and 94.56% respectively with the best features selected from both linear and lasso. The roots activation
function also takes 2.50 seconds computation time versus 3.27 seconds and 2.67 seconds for sigmoid and
hyperbolic tangent respectively and scores better on all other metrics in designing an efficient model to
classify fetal heart disease.
Povzetek: Z metodami strojnega uˇ cenja in genetskih algoritmov je analizirana bolezen srca pri fetusih.
1 Introduction
Cardiovascular disease is growing at a very fast rate and
as per WHO, 30% of world population deaths occur due
to cardiovascular heart diseases, and 23.6 million are ex-
pected to be affected by this disease by 2030 [3]. Cardiac
disease is not only present in adults but can also be present
as a birth anomaly in a newborn child and causes neonatal
fatalities. The heart health of the fetus can be monitored to
detect abnormal heartbeats and predict diseases affecting
the fetus. Thus, predicting the cardiac health of a fetus is
the need of the hour. Cardiotocography is one of the most
commonly used Nonstress Tests which helps in determin-
ing the fetus’s well-being in the womb and during labor.
Cardiotocography consists of uterine contractions and fe-
tal heart rate. Fetal heart rate includes attributes like base-
line heart rate, variations in baseline heart rate, accelera-
tions, decelerations, and uterine contractions. This test is
very useful in studying the base heart rate and uterine con-
tractions pattern and is a vital tool for medical experts to
know when a fetus is suffering from an inadequate supply
of blood or oxygen to the body or any of its parts. As per
the important factors identified by the National Institute of
Child Health and Human Development (NICHD), baseline
heart rate and its variability, accelerations, deceleration and
Nonstress test (NST) are important factors to be considered
while examining the well-being of the fetus [24].
The cardiotocography test is carried out by a device

382 Informatica 45 (2021) 381–392 D. Panda et al.
called Electronic Fetal Monitor [27] which gives two sig-
nals fetal heart rate (FHR) and uterine contractions (UC).
NST and contraction stress test (CST) are two main compo-
nents of a CTG [8]. The NST determines whether the fetus
is distressed and CST determines the placenta’s respiratory
function.
The normal range of FHR baseline lies between 110 bpm
and 160 bpm. If the FHR baseline is higher than 160 bpm
for more than 10 minutes, the fetus is considered to be suf-
fering from tachycardia. On the other hand, if the FHR
baseline is less than 110 bpm for more than 10 minutes
is called bradycardia [6]. Both tachycardia and bradycar-
dia are signs of fetal distress. The conditions are found
out from NST which determines the fetal reactivity i.e. the
interaction between the sympathetic and parasympathetic
autonomous nervous system of the fetus.
Recently machine learning with the use of artificial in-
telligence has become an important and powerful tool for
predicting the heart health of patients. They are effective
in both binary and multi-class classification and are effec-
tive in predicting cardiac disease. One of the effective tools
which are being used for the learning process for single hid-
den layer feeds forward neural networks (SLFNs) is called
extreme learning machine (ELM) [2]. The prime benefit
of ELM is that the hidden layer of SLFNs does not re-
quire tuning and it also has a fast rate of convergence [13].
The learning speed of ELM is considered to be thousands
of times faster than the traditional feed-forward network
learning algorithms [11]. Our study mainly focuses on us-
ing GA for feature selection and studying the accuracy of
ELM using different activation functions.
The following section describes the details of the data
set, implementation of ELM as a Classifier that uses the
best features identified by the Genetic algorithm. The
cross-validation methods used for obtaining the best fea-
tures are studied thoroughly to study the impact of ELM
with four activation functions. The purpose is to study the
effectiveness of the novel activation function by comparing
it with existing activation functions.
2 Methods
2.1 Workflow diagram
The process flow of our proposed model is as described be-
low in Figure 1. The data set is considered with output class
NSP and is pre-processed to remove duplicate entries. Us-
ing GA for obtaining the best features, the model is cross-
validated with 3 regression models and the performance of
ELM is studied before and after feature selection with the
existing and novel activation function.
2.2 Dataset details
The Cardiotocography Data Set, obtained from UCI repos-
itory [9], has been used for our study and experimenta-
tion. The data set originally has 2126 instances with 23
attributes. The CTGs were also classified by three expert
obstetricians into 2 types of classes including the class pat-
tern (1-10) and fetal state class (N=Normal, S=Suspect, P=
Pathologic). The data set has 21 attributes and two out-
put classes. Our experiment is focused on considering all
21 attributes along with one output class. Similar to other
studies conducted on this data set, our experiment also con-
siders 22 attributes where 21 attributes are inputs and the
22nd attribute is the output class “NSP". We have not con-
sidered the other output class “CLASS" for our study. 21
attributes with NSP as the output class, described in Table
1.
2.3 Data pre-processing and splitting of
data sets for model training
Other than the aforementioned 21 features and the out-
put columns, ‘CLASS’ and ‘NSP’, the original database
has 23 other columns, which were removed. Thereafter,
the data set, named ‘DT’, were split into two subset data
sets ‘DT_CLASS’ and ‘DT_NSP’ containing ‘CLASS’ and
‘NSP’ respectively. 12 duplicate rows were deleted, and the
last four rows containing null values were also removed.
The data set of DT_NSP was split to an 80:20 ratio to
train the classifiers on 80% of the data and perform the test-
ing on the remaining 20% of the data.
2.4 Feature Selection and classification
Feature Selection is an important part of designing a pre-
dictive model to reduce unwanted features and also to re-
duce the training time of classifiers. In this paper, the im-
portant features are identified by using the Genetic Algo-
rithm.
The training data set were given as input to ELM with
different activation functions and their accuracy was stud-
ied. Linear, lasso, and ridge regression models have been
used for cross-validation of candidate feature subsets gen-
erated by GA. The attributes selected are considered as best
features and the classification algorithms performance has
been tabulated.
2.4.1 Genetic Algorithm (GA)
The genetic algorithm is a simple Evolutionary search
heuristic algorithm that randomly generates a new popu-
lation. Its basic objective is to find the attributes with max-
imum fitness value in the population [14]. Based on the
Darwinian Principle, it tries to find the fittest individuals.
The entire set of candidate solutions is called a popula-
tion and each solution is called an individual. Our Genetic
algorithm searches for the solution which gives the mini-
mum cross-validation error through linear, lasso, and ridge
regression models. The chromosomes are generated with
fitness values as true or false for each attribute and after it-
erating for the total number of generations the features are
determined which are best fit to predict the outcome. GA

Extreme Learning Machines with Feature Selection Using. . . Informatica 45 (2021) 381–392 383
Figure 1: Process flow diagram to study impact of feature selection on ELM with various activation functions.
depends upon the number of generations, number of chro-
mosomes, number of children created during the crossover,
and best chromosomes. Depending upon the best fitness
values, parents are selected for mating [1]. Crossover has
been carried out with 2 parents and mutated to generate the
new population and the process was repeated for 20 gen-
erations after which the fitness value of features remained
constant. Finally, the features with the best fitness values
are obtained.
Regression models: It is a supervised method in ma-
chine learning to find the correlation of dependant variables
in terms of the independent variables. It is effectively used
for dimensionality reduction of collinear or multi-collinear
variables. The following regression models are used in GA:
Linear Regression: The equation can be written as
shown in Equation 1.
y = 0
+
p
X
k=1
of ik
x
ik
(1)
Ridge: This method uses L2 regularization, where L2
is the penalty equivalent [23] to the sum of the magnitude
of coefficients. This type of regression [22] helps in deal-
ing with a variance that is resultant of the multi-collinearity
of variables. It helps in reducing the variance which is a
resultant of non-linear relationships between two indepen-
dent variables.
Lasso: This model is based on L1 regularization in
which the least related variables are treated as zero. So,
it helps minimize irrelevant features. It adds a penalty to
minimize the loss of a model. L1 is the penalty added to the
sum of the absolute value of coefficients. For the objective
function (Equation 2),
P
N
I=1
off(x
i
; y
i
; ;  )
N
(2)
the lasso regularized version of the estimator will be the
solution to the Equation 3.
min
; of
P
N
I=1
off(x
i
; y
i
; ;  )
N
; subject tok k
1
<t
(3)

384 Informatica 45 (2021) 381–392 D. Panda et al.
Attributes Description
LB Fetal base line heart rate
AC Accelerations per second
FC Fetal movements per second
UC Uterine contractions per second
ASTV percentage of time with abnormal short-term variability
mSTV mean value of short-term variability
ALTV percentage of time with abnormal long-term variability
mLTV mean value of long-term variability
DL mean light decelerations per second
DS mean severe decelerations per second
DP mean prolonged decelerations per second
Width mean histogram width
Min low frequency of the histogram
Max high frequency of the histogram
NMax number of histogram peaks
Nzeros number of histogram zeros
Mode histogram mode
Mean histogram mean
Median histogram median
Variance histogram variance
Tendency histogram tendency: -1=left asymmetric; 0=symmetric; 1=right asymmetric
Table 1: Cardiotocography (CTG) Data set with detail description of attributes.
where only  is penalized while  is free to take any
allowed value, just as  0
was not penalized in the basic
case, andt is a pre-specified free parameter that determines
the amount of regularisation.
The basic algorithm used for feature selection through GA
is as follows:
1. The initial population was randomly initialized by cre-
ating individuals that included/excluded certain fea-
tures. One particular individual may have the chro-
mosome, as shown in Figure 2, where each box repre-
sents a gene, or feature in the data set, green indicates
“True” (the feature is included in the chromosome)
and red indicates “False” (the feature is excluded from
the chromosome).
2. For each generation:
(a) The fitness score is calculated for an individual
as follows:
i. Using regression models, the presence or
absence of the feature is determined. The
target is modeled with the features with val-
ues like 1 for being present and 0 for being
absent.
For example, if the individual illustrated in
Figure 2 is taken, then all the features, ex-
cluding the 2nd, 8th, 12th, and 17th fea-
tures, are taken for modeling.
ii. The cross-validation scores were deter-
mined using negative mean square error
(NMSE), calculated as shown in Equation
4.
NMSE = P
n
i=1
of
 x
i
 P
n
i=1
ofxi
n
 2
n
=
 P
n
i=1
ofx
i
n
 2
 P
n
i=1
ofx
2
i
n
(4)
iii. The mean of the cross-validation scores was
assigned to the fitness value.
(b) The individuals were sorted in the increasing or-
der of their fitness values.
(c) The lastn individuals (which are the bestn in-
dividuals of the population) were selected out of
the population.
(d) In the selected individuals, for number i in the
range of
 n
2
 , thei
th
and (n i)
th
individuals
were crossed as shown in Figure 3.
(e) The daughter chromosome was mutated as
shown in Figure 4 to generate new population:
3. The fittest individual was selected and its genes were
recorded.
2.5 ELM for multi class classification
Extreme Learning Machines are effective single-layer feed-
forward networks (SLFNs) with hidden neurons that do

Extreme Learning Machines with Feature Selection Using. . . Informatica 45 (2021) 381–392 385
Figure 2: A typical chromosome with 21 features
Figure 3: Crossing over of two chromosomes
Figure 4: Daughter Chromosome with 21 features undergoing mutation
not require further tuning [17] and can very effectively be
trained with minimum time for classification, regression,
and feature selection. ELM randomly assigns connections
between the input layer and the hidden neurons and they do
not change further during the learning process. The output
connections are then adjusted to obtain the solution with
minimum cost [12]. There are various types of ELM like
Simple ELM, ELM of ensembles, Pruned ELM, and incre-
mental ELM [17][12].
In our study, a simple ELM is studied with [5][19]
sigmoid, hyperbolic tangent, Fourier and roots activation
functions, and their performances have been compared
based on training time, accuracy, specificity, F measure
score, sensitivity, precision, and AUC.
Basic ELM can be represented as:
For N arbitrary distinct samples (x
i
; t
i
) 2 R
d
 R
m
, SLFNs with L hidden nodes having parameters
(a
i
; b
i
); i2f1; 2; :::; Lg are mathematically modelled
as in Equation 5.
L
X
i=1
of i
g
i
(x
j
) =
L
X
i=1
of i
G(a
i
;b
i
;x
j
)
=o
j
; j2f1; 2;:::;Ng
(5)
Where  i
, is the output weight of the i
th
hidden node
andg(x) is an activation function.
SLFNs approximates these N samples with zero error.
Mathematically, it can be represented as in Equations 2.5
and 2.5
L
X
j=1
ofko
j
 t
j
k = 0 (6)
9 (a
i
; b
i
);  i
j
L
X
i=1
of i
G(a
i
; b
i
; x
j
) =t
j
;j2f1; 2;:::;Ng
(7)
equations and can be written compactly as in Equations
8
H =T (8)
where,
H =
2
6
4
h
1
.
.
.
h
n
3
7
5
=
2
6
6
4
G(a
1
;b
1
;x
1
) ::: G(a
L
;b
L
;x
1
)
.
.
.
.
.
.
.
.
.
G(a
1
;b
1
;x
N
)
.
.
. G(a
L
;b
L
;x
N
)
3
7
7
5
N L
(9)
 =
2
6
4
 T
1
.
.
.
 T
N
3
7
5
L m
(10)

386 Informatica 45 (2021) 381–392 D. Panda et al.
T =
2
6
4
t
T
1
.
.
.
t
T
N
3
7
5
N m
(11)
IfX andY denote the input and output of the function,
W
1
and W
2
denote the weight and bias matrices, and G
denotes the activation function, then for an ELM learning
a model of the form given in Equation 12,W
1
is initialized
randomly andW
2
is estimated as shown in Equation 13
Y =W
2
G(W
1
X) (12)
W
2
=G(W
1
X)
+
Y (13)
where + denotes Moore-Penrose inverse.
Four different non-linear activation functions have been
used in our experiment out of which one function is user-
defined. The list of functions used are mentioned in Equa-
tions 14-17.
Sigmoid Function:
G(a;b;x) =
1
1 +e
 (ax+b)
(14)
Fourier Function:
G(a;b;x) = sin (ax +b) (15)
Hyperbolic Tangent Function:
G(a;b;x) = tanh (ax +b) (16)
Roots Function (User-defined):
G(a;b;x) =
(
0;x =
 b
a
jax+bj
n+1
ax+b
;x6=
 b
a
(17)
wheren2 R is a parameter, which can take any value
between 0 and 1. If the value of n is given as 1, then it
becomes a linear function.
The various activation function graph is attached in
Fig.5.
3 Results
3.1 Experimental setup
All computations are performed on Intel (R) Core (TM) i5-
10210U CPU @2.11GHz with 64bit Windows 10 operating
system. Moreover, Python 3.6.5 software package is used
to simulate the experiments.
3.2 Metrics and analysis
In the CTG dataset, the output class with value 3 is patho-
logical cases, and our experiment focuses on finding out
pathological cases so that they can be used to predict the
heart disease of the fetus.
The features selected from GA by applying linear regres-
sion and lasso for cross-validation, yield the same set of
features and the best 11 features are considered, which are
LB, UC, DS, DP, ASTV , ALTV , MLTV , Width, Max, Me-
dian, and Variance. The model performance is also mea-
sured by applying ridge regression, which considers the
best 12 features, which are LB, UC, DS, DP, ASTV , ALTV ,
MLTV , Min, Max, Nmax, Median, and Variance.
The roots function has been tested with values for n as
0.25, 0.4, and 0.5. The number of hidden units considered
for the study is 200. The various metrics used for compar-
ison include confusion matrix, precision, accuracy, F mea-
sure, and AUC.
The confusion matrix taken for the classification of
pathological cases in the CTG data set is shown in Table
2.
Predicted! Actual# 1 2 3
1 TN TN FP
2 TN TN FP
3 FN FN TP
Table 2: Confusion matrix
where,
– TP: True positive, where output class 3 is predicted as
pathological case
– TN: True negative, where output classes 1 and 2 are
predicted as non-pathological (normal or suspect) case
– FP: False positive, where output classes 1 and 2 are
predicted as pathological case, and
– FN: False-negative, where output class 3 is predicted
as non-pathological case.
The metrics used for measuring classification success are
mentioned in Equations 18-23.
Accuracy =
TP +TN
TP +TN +FP +FN
(18)
Sensitivity =
TP
TP +FN
(19)
Specificity =
TN
TN +FP
(20)
Precision =
TP
TP +FP
(21)
F measure =
2 Precision Sensitivity
Precision +Sensitivity
(22)
AUC =
Sensitivity +Specificity
2
(23)

Extreme Learning Machines with Feature Selection Using. . . Informatica 45 (2021) 381–392 387
Figure 5: Graph of various activation functions in ELM
The inbuilt ELM module python has been compared
with ELM models with sigmoid, Fourier, hyperbolic tan-
gent, and roots (n = 0.25, 0.4, 0.5) based on their accuracy
for predicting heart disease before and after feature selec-
tion. The results are tabulated as shown in Table 3.
The built-in ELM function in python suffers from the
problem of under-fitting for which our study focuses on an
alternate set of activation functions to be used for building
the model. The graph to measure the accuracy of different
activation functions with varying hidden nodes before fea-
ture selection is shown in Figure 6. The user-defined activa-
tion function, which has been named as “roots” is plotted
against the other available functions of ELM. Our model
outperforms other in-built functions in terms of accuracy
in many instances when hidden inputs are varied from 0 to
1000.
When hidden inputs cross 200 units, the graphs of a sig-
moid, hyperbolic tangent and roots activation functions are
almost consistent. The hidden inputs are fixed at 200, to
study other metrics for evaluating the roots function per-
formance.
It is also observed from Table 3, the function of that root
with n=0.4 has given optimum results. The graphs have
been plotted with n=0.4 while using the function of the
root for ELM. Figure 7 shows the graph after using selected
features from linear regression and Lasso in GA. Figure 8
shows the results of ELM with features selected from Ridge
regression in GA.
The results showed that ELM with Sigmoid, Roots and
Hyperbolic tangent activation functions performed better
than the Fourier activation function. The Fourier activation
function is not considered for further study as it is not sen-
sitive to feature selection. The three functions were then
analyzed based on their computation time and other met-
rics for classifying pathological cases. It depicts that the
function of the roots takes lesser time than sigmoid and tan
hyp to compute the results for the testing samples. The ac-
tivation functions of ELM were studied with the original
feature set and a reduced feature set by applying GA and
results have been tabulated in Table 4 and Table 5.
When compared to selected features using GA with
ridge, GA with Linear and Lasso regression yielded better
performance on all metrics. The graph in Figure 6 shows
the performance of the 4 activation functions before ap-
plying feature selection. The performance of ELM mod-
els improved after feature selection using the three activa-
tion functions sigmoid, hyperbolic tan, and roots(n=0.4).
The roots activation function performed better than the
other 2 activation functions. Figure 7 depicts the graph
for measuring the performance of ELM after feature se-
lection. The best features derived from Linear and Lasso
cross-validation in GA have been used to plot the graph.
Another graph shows the improved performance of ELM
after using feature selection from best features obtained
from Ridge with GA and is shown in Figure 8. The results
are dependent upon the number of hidden inputs taken and
can change. It has also been observed that by varying the
hidden inputs from 0 to 1000, ELM with roots activation
function, has outperformed in classification in the majority
of the cases.
The standard inbuilt ELM remains unaffected and even
the performance degrades after feature selection. Its per-
formance reduced from 11.11% to 10.17% after using fea-
ture selection. However, the customized ELMs show im-
provement in terms of accuracy. The ELM with sigmoid
function has improved from 92.67 to 94.56%, with hy-
perbolic tangent function the performance improved from
93.14 to 94.56% and with roots activation function (n=0.4)
the accuracy improved from 94.33 to 96.45%. The ELM
with Fourier activation function remains unchanged before
and after feature selection and has given 90.07% accuracy
throughout the experiment.
4 Discussion
Various fetal disease prediction systems have been pro-
posed for diagnosing the fetus’s health. The works have
been carried out on Cardiotocography data set using either
all the features or a subset of them. One of the study [30]
focuses on using various types of ANN like MLPNN, PNN,
and GRNN models using the entire data set to identify the
fetal state and have reported the overall classification accu-
racy’s for MLPNN, PNN, and GRNN as 90.35, 92.15, and

388 Informatica 45 (2021) 381–392 D. Panda et al.
Classifier Before Feature Selection
After Feature Selection
Linear Lasso ( = 0.0001) Ridge ( = 0.0001)
ELM inbuilt in Python 11.11 10.17 10.17 10.16
ELM with new activation function(n=0.25) 94.56 95.98 95.98 95.74
ELM with new activation function(n=0.4) 94.33 96.45 96.45 95.04
ELM with new activation function(n=0.5) 94.8 96.21 96.21 95.04
ELM with sigmoid activation 92.67 94.56 94.56 94.33
ELM with Fourier activation 90.07 90.07 90.07 90.07
ELM with hyperbolic tangent activation 93.14 94.56 94.56 93.85
Table 3: Classification Performance of ELM (Accuracy%) with best features obtained by applying Genetic Algorithm
(GA) using Linear, Lasso and Ridge regression models.
ELM activation functions /
sigmoid roots (n=0.4) Hyperbolic tangent
Original DT Best features (11 attributes) Original DT Best features (11 attributes) Original DT Best features (11 attributes)
Confusion Matrix
294 29 2
26 28 2
4 23 15
299 25 1
24 30 2
2 18 22
306 19 0
21 32 3
4 17 21
301 24 0
24 32 0
2 13 27
295 30 0
24 30 2
3 24 15
299 26 0
24 29 3
1 19 22
Accuracy 92.67 94.56 94.33 96.45 93.14 94.56
Sensitivity 35.71 52.38 50.00 64.29 35.71 52.38
Specificity 98.95 99.21 99.21 100.00 99.48 99.21
Precision 78.95 88.00 87.50 100.00 88.24 88.00
F-measure 49.18 65.67 63.64 78.26 50.85 65.67
AUC 67.33 75.80 74.61 82.14 67.59 75.80
Computation Time in secs 3.05 3.27 2.31 2.50 2.67 2.67
Table 4: Measurement Metrics for Sigmoid, Roots, and Hyperbolic Tangent activation functions before and after feature
selection by GA through linear/lasso regression with 200 hidden inputs (in %).
ELM activation functions /
sigmoid roots (n=0.4) Hyperbolic tangent
Original DT Best features (12 attributes) Original DT Best features (12 attributes) Original DT Best features (12 attributes)
Confusion matrix
294 29 2
26 28 2
4 23 15
295 25 1
25 30 1
6 16 20
306 19 0
21 32 3
4 17 21
297 27 1
21 33 2
2 16 24
295 30 0
24 30 2
3 24 15
294 30 1
25 30 1
6 18 18
Accuracy 92.67 94.33 94.33 95.04 93.14 93.85
Sensitivity 35.71 47.62 50.00 57.14 35.71 42.86
Specificity 98.95 99.48 99.21 99.21 99.48 99.48
Precision 78.95 90.91 87.50 88.89 88.24 90.00
F-measure 49.18 62.50 63.64 69.57 50.85 58.06
AUC 67.33 73.55 74.61 78.18 67.59 71.17
Computation Time in secs 3.05 3.17 2.31 2.55 2.67 2.71
Table 5: Measurement Metrics for Sigmoid, Roots, and Hyperbolic Tangent activation functions before and after feature
selection by GA through ridge regression with 200 hidden inputs (in %).

Extreme Learning Machines with Feature Selection Using. . . Informatica 45 (2021) 381–392 389
Figure 6: ELM accuracy before feature selection with Sigmoid, Fourier, Hyperbolic tangent(tanhyp) and Roots(user-
defined) activation functions.
Figure 7: ELM accuracy with sigmoid, Fourier, hyperbolic tangent (tanhyp) and roots(user-defined) activation functions,
after feature selection with best 11 features selected through GA with linear/lasso regression for cross-validation.
91.86%, respectively. Another work proposes using Dis-
criminant Analysis, Decision Trees, and Artificial Neural
network for identifying the fetal status [15] using all fea-
tures of the CTG data set and have reported 82% accuracy
for DA, 86.4% for DT, and 97.8% accuracy for ANN. The
work also establishes the fact that giving rules for identifi-
cation is always better i.e DT even with lower accuracy is
better interpretative for results rather than an Artificial neu-
ral network which resembles a black box where processes
involved are unknown. Another work including all the fea-
tures [31], focuses on studying fetal well-being using The
Least Square SVM method with Particle Swarm Optimiza-
tion and Decision Trees. This method yielded 91.62% ac-
curacy with all 2162 instances and had been validated using
10-fold cross-validation. The PSO played a major role in
optimizing the penalty factor of LS-SVM. A similar work
proposed using Adaptive Neuro-Fuzzy inference Systems
(ANFIS) [21] to differentiate pathological cases from nor-
mal ones and reported accuracy of 97.2% for normal cases
and 96.6% accuracy for pathological states. Rough Neural
Networks suggested in another study for fetal risk assess-
ment [4] was provided with upper and lower boundaries in
input layer as well as hidden layers and gave an accuracy
of 92.95% for pathological cases using the entire set of fea-
tures.
The above works have reported a maximum accuracy of
97.8% and have used all the features for the experiment. In
comparison, our work has yielded 96.45% accuracy with
only 11 features, thus reducing the computation cost and
time.
Our paper focuses on studying the efficiency of ELM
with novel activation function for the effective classifica-
tion of fetal heart disease. The accuracy of our model is
compared before and after feature selection using GA. GA
uses regression models for cross-validation of the best fea-
tures and linear as well lasso have yielded the same 11

390 Informatica 45 (2021) 381–392 D. Panda et al.
Figure 8: ELM accuracy with sigmoid, Fourier, hyperbolic tangent (tanhyp) and roots(user-defined) activation functions,
after feature selection with best 12 features selected through GA with ridge regression for cross-validation.
best features in our experiment and have given better accu-
racy than the 12 features selected with ridge regression. As
compared to a study [29], the features selected by convolu-
tion neural networks, MKNet and MKRNN which resulted
in classification accuracy of 90%, our feature selection has
given better performance and accuracy has improved by al-
most 6%.
For classification of heart disease, extraction of impor-
tant features plays an important role as evident from [28],
[20], [32]. Generalized discriminant analysis has been used
with the Radial basis kernel function or Gaussian function
of ELM to analyze heart rate signals and the process has
achieved 100% accuracy. The impact of feature selection
was therefore explored in our study using GA as GA in the
study [20]. gave improved results for the classification of
heart disease. Our accuracy also improved from 94.33%
to 96.45% after using the best features obtained from GA
with a lasso and linear regression models, similar to [32]
where accuracy improved by 5.6% using PCA.
Our model has given improved results of 96.45% accu-
racy as compared to another work [7] which used ANN
with ELM for classifying fetal heart disease and have given
93.42% and 91.84% using ELM and ANN respectively.
ELM with our novel activation function roots (n=0.4),
also outperformed the results of classification using vari-
ous other classifiers given in [10], where XGBoost gave the
best results with (>92%) and was comparable with other
optimized ELM models used for classification of various
other diseases.[18]
The number of hidden inputs in our study has been con-
sidered to be 200 as compared to 2 to 3 input units sug-
gested in the work [16] and the inputs have been selected
by varying the units from 0 to 1000 and the optimized value
has been considered for the ELM.
Our novel approach gave 100% specificity and 100%
sensitivity as compared to other classification models [26].
The best features selected by other studies [25] are also the
common features that have been selected by using GA with
cross-validation using linear and lasso and have obtained
accuracy>2% as compared to classification and regression
decision trees and Self-organizing maps.
5 Conclusion
ELM with sigmoid and roots activation functions produced
accuracy above 95%. ELM takes less time than other neu-
ral networks to get trained as their input weights and biases
do not need to be tuned further, but it depends on the ac-
tivation function used. In this experiment, the function of
the roots was faster than other functions when hidden units
were set to 200. The Genetic Algorithm has played an im-
portant factor in improving the accuracy of ELMs through
feature selection. Other activation functions can also be
used to see the effect on various parameters for classify-
ing pathological cases in Cardiotocography data sets. The
models can be used as an effective tool to aid medical ex-
perts in detecting cardiological abnormalities in the fetus.
Future work can be carried out on the optimization of
various other activation functions of ELM to analyze the
impact of the selection of hidden units on computation time
and accuracy. Depending upon the dataset, the number of
hidden units, and the number of generations, the activa-
tion function can be optimized to find the value of n in the
function of the user-defined roots, which will determine the
best results. The current study has used regression tech-
niques for cross-validation in GA and in the future other
techniques can be used to examine the model.
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