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• Information provided byeach kind of data must be evaluated and assigned for diagnostic processes. To simplify the diagnostic process and evade errors in that process, artificial intelligence techniques can be adopted like computer-aided diagnosis and artificial neural networks. • Thebiostatistical services machine learning algorithms can deal with a broad set of specific data and produce categorized outputs by checking the blogs in Pubrica Full Information: https://bit.ly/3mkl0zZ Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/ Why Pubrica? When you order our services, we promise you the following – Plagiarism free, always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts. Contact us : Web: https://pubrica.com/ Blog: https://pubrica.com/academy/ Email: [email protected] WhatsApp : +91 9884350006 United Kingdom: +44- 74248 10299
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Copyright © 2020 pubrica. All rights reserved 1
Overview of Artificial Neural Network in Medical Diagnosis
Dr. Nancy Agens, Head,
Technical Operations, Pubrica
In-Brief
A massive volume of clinical data is
produced daily that possess minute and
critical information as well as varied, in-
depth concepts of biochemistry and the
results of imaging devices. Information
provided byeach kind of data must be
evaluated and assigned for diagnostic
processes. To simplify the diagnostic
process and evade errors in that process,
artificial intelligence techniques can be
adopted like computer-aided diagnosis
and artificial neural networks. The
biostatistical services machine learning
algorithms can deal with a broad set of
specific data and produce categorized
outputs by checking the blogs in Pubrica.
Keywords: Biostatistics Services, clinical
biostatistics services, biostatistics
consulting services, biostatistics CRO,
Statistical Programming Services,
Biostatistical Services, biostatistics
consulting firms, Biostatistics for clinical
research, statistics in clinical trials,
biostatistics in clinical trials, Biostatistics
CRO, Biostatistics Support Service,
Clinical Biostatistics Services
I. INTRODUCTION
The artificial neural network has
been widely used in the fields of science
and technology. It is used for the
optimization of data. It predicts the outputs
using the input data in fields like chemical
engineering, biotechnology, healthcare,
agriculture, etc., which all handles varied
sets of data. The artificial neural network
can be used for modelling non-linear
systems with a complex system of
variables. Thus, most of the chemical
engineering and biological processes are
modelled using Artificial neural network
with the help of biostatistical consulting
services.
II. ARTIFICIAL NEURAL NETWORK
Clinical biostatistics services state
that Artificial neural network is the
simulation of human neural architecture.
The learning and generalization potentials
of human neural network inspired for the
development of an artificial neural
network. It works by taking the 70% of
input data to build a network then takes the
remaining 15% data to train itself and at
last utilize the remaining 15% data to test
itself and eventually produce the optimized
outputs.
III. ARCHITECTURE
The artificial neural network is
made up of three layers, viz., – (i) input
layer, (ii) hidden layer, (iii) output layer.
The schema of the neurons built inside the
network is based upon the complexity of
the system. The input layer collects the
input data and transfers to the hidden layer
where the data is processed to produce
optimized results with statistical
programming services. Every Artificial
neural network has an activation function
that is used for determining the output.
Each neuronisinterconnected, and each
connection has a weight attached
possessing either positive or negative
Copyright © 2020 pubrica. All rights reserved 2
value which tends to change upon the
training the network.
IV. OVERVIEW OF ARTIFICIAL
NEURAL NETWORK IN MEDICAL
DIAGNOSIS
Seeking various uses in various
fields of science, medical diagnosis field
also has found the application of artificial
neural network using biostatistics in
clinical services. It is used in the diagnosis
of cancer, sclerosis, diabetes, heart
diseases, etc. An adaptive algorithm is
developed and applied to yield maximum
accuracy in outputs with the statistics in
clinical trials.
V. CARDIOVASCULAR DISEASES
It is the collection of diseases
affecting the heart, cardiac muscles, blood
vessels, veins. National centre of health
statistics reported that leading cause of
death in united states of America is these
cardiovascular diseases. In the past, the
data collected from the patients were used
to develop an Artificial neural network
model with the backpropagation algorithm
was developed. This model was able to
achieve 91.2% accuracy in the diagnosis of
these diseases from the data collected.
There were other models with less than
90% accuracy also used to
diagnosespecific types of heart diseases.
VI. CANCER
In 2012, reports of American
cancer society said that more than 1.6
million newly diagnosed cases were found.
Hence, there was the need to develop a
rapid and appropriate diagnosis for clinical
management. The pertinent information
for diagnosis was collected from the
advanced analytical methods like mass
spectrometry and applied in the clinical
diagnosis of breast and ovarian cancer.
Artificial neural network is also used to
develop in diagnosing the different types
of brain tumours, lung carcinoma.
Ultimately, Artificial neural network was
seen using the ground-level data that
ranges from clinical data to results of
biochemical assays and providing
maximum diagnostic accuracy for different
types of cancer.
VII. DIABETES
Diabetes has become a severe
health risk issue in both developed and
developing countries that reaching an
estimate of 366 million diabetes cases
globally. Type ii diabetes is the standard
type of this disease which is due to the
improper cellular response to insulin
which leads to hyperglycemia. The
information of parameters like age, gender,
weight and glucose level were collected
and used as input data for building an
Artificial neural network which could able
to produce results with 90% accuracy.
Artificial neural networks are used to track
the level of glucose as well as diagnosing
diabetes according to biostatistical
research for clinical trials.
VIII. CONCLUSION
The artificial neural network can be
inferred as a powerful tool in clinical
management of diseases with several
advantages like the capability of
processing a vast set of data, reducing the
processing time, ability to produce
optimized results with maximum accuracy.
Nevertheless, Artificial neural network can
be used only as tool aiding in diagnosis
done by the clinical physician, says
biostatistical CRO, who is responsible for
Copyright © 2020 pubrica. All rights reserved 2
critical evaluation of the results. Pubrica
helped to understand the role of ANN tool
in the medical field.
REFERENCES
1. Al-Shayea, Q. K. (2011). Artificial neural
networks in medical diagnosis. International
Journal of Computer Science Issues, 8(2), 150-
154.
2. Amato, F., López, A., Peña-Méndez, E. M.,
Vaňhara, P., Hampl, A., & Havel, J. (2013).
Artificial neural networks in medical diagnosis.
3. Baxt, W. G. (1991). Use of an artificial neural
network for the diagnosis of myocardial
infarction. Annals of internal medicine, 115(11),
843-848.