International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
A Review on Mental Health using Soft Computing and NeuroFuzzy Techniques
Nandita Bhanja Chaudhuri1, D.Chandrika2, D. Kamal Kumari3
1
IT Department, Assistant Professor, Vignan's Inst. of Engg. for Women
Visakhapatnam, Andhra Pradesh, India
2
CSE Department, Assistant Vignan's Inst. of Engg. for Women
Visakhapatnam, Andhra Pradesh, India
3
CSE Department, Assistant Vignan's Inst. of Engg. for Women
Visakhapatnam, Andhra Pradesh, India
Abstract-Mental health awareness is very crucial for
any society. To spread the education of mental
health, World Mental Health day is celebrated on
10th October which was first celebrated in 1992 by
World Federation for Mental Health, a world-wide
mental health organization. It has thousands of
followers, members, and contacts across 150
countries. Nowadays, e-health is a common concern
and societal involvement is salutary. This article
explored the areas of mental health using soft
computing and neuro-fuzzy techniques and presented
in the form of a review.A persistent search,
conducted in April 2015, from many renowned
bibliographic databases and search engines like
Google, MEDLINE, Wikipedia,
AIDSDRUGS,
AIDSTRIALS, HISTLINE, HSRPROJ, and SDILINE
which 150 unique papers and abstracts.Twenty two
articles met the inclusion criteria for the review.
Several techniques for identifying and diagnosing
mental health were encountered like regression,
genetic algorithm, Artificial Neural Network,
probabilistic neural network analysis, fuzzy sets,
fuzzy logic, hybrid tools etc. Specifically, this review
focuses on mental health with respect to the
implementation of soft computing and neuro-fuzzy
techniques. This review could act as a suggestion for
the doctors and researchers. The techniques
mentioned above could be beneficial for identifying a
disease in a much better way through various tools
and techniques of soft computing and neuro-fuzzy
techniques. It serves wide range of results that are
the outputs of the existing research and development
and could be mobilized to further enhance it.
KeywordsGoogle,
MEDLINE,
Wikipedia,
AIDSDRUGS, AIDSTRIALS, HISTLINE, HSRPROJ,
SDILINE, Soft computing, Neuro-Fuzzy techniques.
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I.INTRODUCTION
Mental health is a very important part of any
human being as it guides to an emotional, social well
being, and the adaptability towards a changing
environment. So, mental health is significant as
physical health. It can affect day-to-day activities of
an individual. Mental disorder reckons one-third of
the world's disability due to human's health problem,
exhibiting societal and personal sufferings which
leads to huge social and economic costs [1] [2]. So,
identifying and diagnosing the diseases are very
crucial.
The recent trend is e-health that influences
electronic processes and communication to manage
healthcare information. E-health is responsible for
proper utilization of communication, information,
business and transaction. It enables a secure
maintenance of records among physicians and
patients and optimizes their expenditure and travels
[3].
In computer science, various soft computing
and neuro-fuzzy techniques are applied to diagnose
and classify various disorders in mental health. Soft
computing techniques are capable of making
decisions, draw useful conclusions from the world
around us, work with partial truth data, and learn
from heuristics or expert's data such as Fuzzy
Techniques and Agent based Systems show
promising research directions. The main parts of soft
computing are Fuzzy Systems (FS), Fuzzy Logic
(FL), Evolutionary Computation (EC), Neural
Network (NN), Neural Computing (NC), Genetic
Algorithm (GA), Machine Learning (ML), and
Probabilistic Reasoning (PR) [4]. Fuzzy systems can
accept data that lies in certain range in the universal
data. The region plays the part of a fuzzy set. It
describes a mapping from such universe into the real
numbers [0, 1] instead of the set {0, 1} [5].
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Potentially, the field may fulfill the dream of artificial
intelligence: a computer capable of making decisions
and serving as an expert system [6]. Neural networks
consist of various layers. Each layer contains finite
nodes with activation function. Inputs are submitted
to the input layer. It directs the data to the hidden
layers using weighted connection [7]. Finally, the
output is assembled at output layer, as shown in
Fig.1.
that capture the best features of two parents and pass
it to a new off-spring string. Mutations are
probabilistic operators that try to introduce needed
solutions features in populations of solutions that lack
such feature [10]
Fig.2. Process of Genetic Algorithm.
Fig.1. A Neural Network.
In neural computing knowledge resides in
the weights or 'connections' wij between nodes (hence
the
older
name
for
neural
computing,
'connectionism'). The net's weights are equivalent in
biological terms to synaptic efficiencies though they
are allowed to change their values in a less restricted
way than their biological counterparts. The
representation of this knowledge is distributed: each
concept stored by the net corresponds to a pattern of
activity over all nodes so that in turn each node is
involved in representing many concepts. The weights
are learned through experience, in a usually iterative
procedure using an update rule for the change in the
weight Δwij [8] [9]. Genetic algorithms are defined as
a programming paradigm used to solve NP-hard
problems by performing a randomized search in the
solution space. Genetic algorithms encode the
solution to a given problem in a binary (or realvalued) string. Each string's element represents a
particular feature in the solution. The string (solution)
is evaluated by a fitness function to determine the
solution's quality: good solutions survive and have
off-springs, while bad outputs are terminated.
Solution's constraints can be modeled by penalties in
the fitness function or encoded directly in the
solution data structures. To improve current
solutions, the string is modified by two basic types of
operators: Cross-over and Mutations shown in Fig.2.
Cross-over's are (sometime) deterministic operators
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Machine learning is a part of computer
science that evolved from the study of pattern
recognition and computational learning theory in
artificial intelligence. Machine learning explores the
construction and study of algorithms that can learn
from and make predictions on data. Such algorithms
operate by building a model from example inputs in
order to make data-driven predictions or decisions,
rather than following strictly static program
instructions [11]. The purpose of a probabilistic logic
or probability logic or probabilistic reasoning is to
combine the capacity of probability theory to handle
uncertainty with the capacity of deductive logic to
exploit structure. The result is a richer and more
expressive formalism with a broad range of possible
application areas. Probabilistic logics attempt to find
a natural extension of traditional logic truth tables:
the results they define are derived through
probabilistic expressions instead. A difficulty with
probabilistic logics is that they tend to multiply the
computational complexities of their probabilistic and
logical components. Other difficulties include the
possibility of counter-intuitive results, such as those
of Dempster-Shafer theory. The need to deal with a
broad variety of contexts and issues has led to many
different proposals [12]. To enable a system to deal
with cognitive uncertainties in a manner more like
humans, one may incorporate the concept of fuzzy
logic into the neural networks. The resulting hybrid
system is called fuzzy neural, neural fuzzy, neurofuzzy or fuzzy-neuro network [13].
II.BACKGROUND
This literature review is a part of a larger
project 'Detecting mental health using soft computing
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techniques'. The project is conducted by group of
people aimed to collect mental health data and apply
soft computing and neuro-fuzzy techniques to detect
the disorders. This review proves the significance of
mental health as well as the previous research work
and their outcomes.
III.SELECTION OF ARTICLES
Computer application and e-health in a
positive light and public engagement with these
services is generally useful [14]. A persistent search
from many renowned bibliographic databases and
search engines like Google, MEDLINE, Wikipedia,
AIDSDRUGS,
AIDSTRIALS,
HISTLINE,
HSRPROJ, and SDILINE which 150 unique papers
and abstracts. The search techniques were developed
in stages. The term used in the initial search were:
'Mental health' (10 articles returned); 'Mental
disorder' (3 articles returned); 'Mental illness' (1
article returned); 'Mental sickness' (1 article
returned); 'Soft computing and mental health' (20
articles returned)'; 'soft computing and mental
disorder' (3 articles returned); 'Soft computing and
depression' (3 articles returned)'; 'Fuzzy systems and
mental health' (2 articles returned)'; 'Fuzzy logic and
mental health' (2 articles returned)'; 'Evolutionary
computation and mental health' (1 article returned)';
'Neural network and mental health' (7 articles
returned)'; 'Neural computing and mental health' (1
article returned)'; 'Genetic algorithm and mental
health' (3 articles returned)'; 'Machine learning and
mental health' (2 articles returned)'; 'Probabilistic
reasoning and mental health' (2 articles returned)';
'Neuro-fuzzy technique and mental health' (4 articles
returned)'. The mined keywords are allocated in the
three categories:
Mental health, e.g. depression, Alzheimer's
disease,
autism.
Computer applications, e.g. soft computing,
neurofuzzy techniques.
Evaluation, e.g. implementation of various
computer
applications, accuracy of detection of mental
health.
The following categories were excluded:
Blogs, conference reports, book reviews,
editorials, and viewpoints.
IV.FINDINGS AND DISCUSSIONS
The initial search returned 45 items via
MEDLINE, 70 via Wikipedia, 200 via Google, and
300 via all other combined search engines and
bibliographic databases. After removal of the
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redundant matters 150 articles were selected. Further
filtration by the reviewers for context-based search
has lead to 22 finalized articles.
Chattopadhyay [15], proposed PCA as one
useful analytical technique in multidimensional data
to extract the hidden features from it because PCA is
independent of target data and learns without the
necessity of supervision. A further study
demonstrated the severity of the depression which is
done by fuzzy neural hybrid approach which helps in
diagnosing the depression severity by Mamdani's
fuzzy logic controller. The average accuracy of the
hybrid system is seen to be 97.50\%.
Fekihal et.al. [16], proposed an approach
entitled A Self-Organizing Feature Map (SOFM)
attains practical and task-relevant diagnostic
categories by use of clustering techniques. In this
approach classified transcribed speech samples and
determines mental disorders. An unsupervised
Artificial Neural Network was implemented using the
NeuroSolution. The classifier determines whether an
individual have mental disorder or not. The proposed
approach demonstrated that all the categories are
identified and classified, with an accuracy of (97) to
get the appropriate output.
Dasri et.al. [17], proposed application of soft
computing techniques to automate depression
diagnosis. In order to achieve the goal to automate
the diagnosis of depression, an intelligent NeuroFuzzy model has been developed. It has been trained
with a real-time depression data. The test data
suggests that the Mean Squared Error in prediction is
nominal for most of the cases. Such a system could
assist the doctors to take decisions in much needed
situations.
Mei et. al. [18], proposed an intelligent
diagnosis system, which is capable of diagnosing 61
kinds of mental child health disorders like
hyperactivity, conduct disorder, phobia, mental
retardation, depression, tic disorder, pervasive
developmental disorders, etc. The system has
implemented Back Propagation learning algorithm
for detecting the diseases. After detection, a proper
diagnosis method is also suggested by the system.
Some of the parameters that were considered during
the experiment were input node number, hidden node
number, output node number, control strategy,
transfer function, network connection mode, and train
count. Recall result range and recall error range were
calculated. Finally, the outcomes of this system are
compared with the diagnosis of the senior child
psychiatrist's which is found to be 99\% consistent.
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Khosrowabadi et.al. [19], presented a
biologically inspired feedforward neural network
system which discriminates emotion from
electroencephalogram (EEG) is a measure of brain
waves based on valence and arousal levels. The EEG
data from the perception of emotional stimuli of
healthy participants are collected. The top-down
approach is formulated and bottom-up approach is
bypassed using SAM answers. Subsequently, the
performance of the proposed neural network for
discriminating emotions is evaluated using the EEG
data and SAM responses. The results show that there
are patterns of brain regions connectivity in the
perception of individual emotional stimuli. These
patterns are detectable by estimating the connectivity
between different brain regions from the EEG data.
However, these patterns vary in different subjects but
common patterns can be selected at specific
frequencies.
Nevertheless,
the
feedforward
architecture is presented by considering a constant
level of attention, mood, and mental health for all the
subjects. Therefore, further assessment for
understanding the impact of attention level, moods,
and mental disorders on the perception of emotional
stimuli should be done. In addition, improvement of
the network for a multiclass valence/arousal problem
is proposed for future works.
Autism is one such mental illness that
affects the social and personal well-being. Ahuja and
Kaur [20], proposed a neuro-fuzzy techniques that is
used to predict patient autism level depending on
various parameters like social, communications,
emotional, and behavioral levels. These parameters
are very important and hence useful for diagnosing
the disease. Their method is as follows: Patient
characteristics are provided as input; the parameters
are identified based on which analysis would be
performed; the dataset is divided into training and
testing set; the dataset are trained using neural
network; and finally, the fuzzy rule is defined on
parametric values to identify the severity of the
disease.
Mukherjee et. al. [21], modeled a depression
data by Feed Forward Neural Network and Radial
Basis Function Neural Network. They proposed a
method that could handle the uncertainties of soft
computing techniques. Real life medical data had
been collected. A multi- layered feed forward neural
network was built. The input layer accepts ten mental
health symptoms. The hidden layer accepts the
weighted mean which is the output of the first input
layer. The weight set is randomly generated by seed
based random number generator. The optimized
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weight values are used after the training process. The
nodal outputs vary within 0 and 1 since the logsigmoid transfer function is used. The nodal output of
this layer is as follows:
OP2 = 1/(1+exp(-a*IP2))
(1)
Where IP2 is nodal input, OP2 is nodal output, and a is
a constant.
The output layer accepts the weighted mean
of the hidden layers. The transfer function is tansigmoid and thus the nodal output varies within 0 and
1. The nodal output of this layer is as follows:
OP3 = (exp(-IP3)-exp(-IP3))/ (exp(-IP3)+exp(-IP3))
(2)
Where IP3is nodal input, OP3 is nodal output.
Tuning of the neural network system is done
using Back Propagation algorithm. The Radial Basis
Function had used input and output layer nodes. The
hidden layer used Radial Basis transfer function. The
nodal output is as follows:
OP2R = I2P2R log (IP2R)
(3)
Where IP2R is nodal input, OP2R is nodal output.
Scott et. al. [22], proposed an integrated
model for conceptualizing and understanding mood
disorders drawing upon a broad literature. This
integrated model of emotion processing and
regulation incorporates the linguistic constructs of the
Research Domain Criteria (RDoC) initiative. In
particular, they focused on the positive valence
domain/circuit (PVC), highlighting recent reward
research and the negative valence domain/circuit
(NVC), highlighting rumination. Furthermore, they
also illustrated the Cognitive Control and Problem
Solving (CCaPS) circuit, which is heavily involved in
emotion regulation, as well as the default mode
network (DMN) and interactions between circuits.
They concluded by proposing methods for addressing
challenges in the developmental study of mood
disorders, including using high-risk design that
incorporates risk for many disorders.
V. CONCLUSIONS AND
RECOMMENDATIONS
This review draws together literature on
mental health using soft computing and neuro-fuzzy
techniques with three main areas: mental health, soft
computing, and neuro-fuzzy techniques. The findings
of this review augment and support the findings of
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earlier literature reviews. Various soft computing and
neuro-fuzzy techniques are encountered and their
outputs are observed regarding the detection and
diagnosis of mental health techniques. It can be
recommended to build a system using soft computing
and neuro-fuzzy technique that could have higher
accuracy and capability to detect and diagnose the
disease in a better way.
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