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Fundamental Steps in Artificial Neural Networks-Based Medical Diagnosis

Dr. Nancy Agens, Head,

Technical Operations, Pubrica

sales@pubrica.com

In-Brief

An extensive source of information is

available to all the medical professionals

starting from symptoms to biochemical

analysis using imaging devices. The

artificial neural network is an AI-based

medical diagnostic tool used to evaluate the

vast amount of data says medical

Manuscript Peer Reviewing Services. The

process of performing an artificial neural

network for medical analysis must be

appropriate and relevant. Pubrica briefly

explains the abilities, procedure,

limitations of artificial neural networks in

medical diagnosis.

Keywords: Medical Manuscript Peer

Reviewing Services, Pre peer review, peer

review service, pre-submission peer review,

Peer review report, medical peer review

services, peer-reviewed articles, Peer-

reviewed publication.

I. INTRODUCTION

The process of artificial neural network

analysis rises from the clinical situations that

give a brief overview of the purposes of

medical diagnosis with enormous

confidence. Pre peer review for the neural

network observes the patient's data to detect

a specific disease. Once after finding the

target, the next step is to proceed the

experiment with laboratory and

instrumentation processes that give

information about the patient's health

conditions. There are many ways to perform

these processes. Many tools are there to

perform the tasks. However, careful

selection of tools is essential to avoid noise-

based instruments in the first stage. The next

stage is to formulate a database and to

validate it. Likewise, there few significant

steps involved in performing artificial neural

network analysis, and this blog explains the

steps in the artificial neural network using

peer review service.

II. STEPS INVOLVED IN AN

ARTIFICIAL NEURAL NETWORK

Features selection

Building the database

Data cleaning and preprocessing

Data homoscedasticity

Training and verification of database

using artificial neural network

Network type architecture

Training algorithm

Verification

Robustness of artificial neural

network-based approaches

Testing in medical practice

III. FEATURES SELECTION

Diagnosis ofany specific disease depends on

various data. Medical professionals will

extract the relevant data from each type of

information to detect the most comfortable

diagnosis. For artificial neural network

analysis, a collection of data is known as

'Features' that can be symptoms,

phytochemical analysis, or any other

relevant information helps for diagnostic

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purposes. So it is closely related to the final

diagnosis. The significance of Artificial

neural networkis to grasp from previous

samples, makes them very flexible and

potential tools to perform medical diagnosis.

Few types of the artificial neural networkare

acceptable for solving problems while other

data modelling process are more efficient in

approximation. Robust indicators should

help train neural networks using the clinical

situation or pathology. Feature selections

depend on the previous clinical case. There

are a few limitations for features before the

selection processes given by peer review

report.

IV. LIMITATIONS

Insufficient

Non-specific

Noisy information about the problem

Redundant

The selection of appropriate features is

essential for medical diagnosis using

different medical approaches with the help

of medical peer review services.

V. TOOLS USED

Mathematical means of data

Genetic algorithm

Principal component analysis

VI. BUILDING THE DATABASE

The "example" cases are a unified database

for the neural network is training. An

"example" provides patient values for the

selection, collection and evaluation. The

training quality and resultant generalization,

the prediction capability of the network,

firmly based on the training database. The

database must have enough number of

relevant "examples" and allow the system to

learn extracting the structure hidden in the

dataset. Also, clinical laboratory data must

be in a document that is rapidly transferable

to programs in computer-aided diagnosis

using peer-reviewed articles.

Data cleaning and preprocessing

Preprocess the training database before the

evaluation process by the neural network.

Scale the data between the interval for the

logistic purpose. Besides, the demonstration

of a few cases, some data are missing and

remove it from the database set to improve

the performance of the neural network.

There occurs a decrease in the performance

of the system for imbalanced databases.

Data homoscedasticity

Evaluation of a training network for new

patients by

The suitable features

Database

Data preprocessing method

Training algorithm

Network architecture

Data concerning

Homoscedasticity may lead to failure and

misclassify the original data. To overcome

this problem, us additional parameters that

belong to a particular sample indicating the

population.

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VII. TRAINING AND VERIFICATION OF

DATABASE USING ARTIFICIAL

NEURAL NETWORK

Network type and architecture

There are many other network models such

as Bayesian, recurrent, or fuzzy, stochastic

but multilayer feed-forward neural networks

are most common. The optimal network

architecture selection is the first stage. The

testing networks using a various number of

hidden layers and nodes use them. It gives

the optimal architecture for which the

minimum value of E for both training and

verification.

Training algorithm

Different types of training algorithms are

available."Network learning" section, use of

two training parameters:

(i) learning rate

(ii) momentum.

Verification

Verification of dataset from various data for

training for the artificial neural networks-

based medical diagnosis is there in the

process.

Robustness of artificial neural

network-based approaches

The artificial neural network can tolerate a

level of noise in the data. Consequently,

they give sufficient prediction accuracy.

This noise may sometimes cause false

results, mainly when modelling a

complicated system like the health condition

of a human body.One of the best ways to

avoid this is to perform the process by an

experienced clinician knowing the

discriminative power of the artificial neural

network systems having a peer-reviewed

publication.

VIII. TESTING IN MEDICAL PRACTICE

The final step in the artificial neural

network-aided diagnosis is testing medical

practice. Examination of New patient, the

network's outcome must be careful. Medical

data of patients must be correct when

including it in the training database. The

comprehensive and extensive evaluation of

ANN diagnosis applications in the clinical

sector is necessary for different institutions.

Only verified ANN medical diagnosis

applications in the clinical industry are an

essential condition for future expansion in

medicine.

IX. CONCLUSION

The ANN is a powerful tool for physicians

to perform diagnosis. It has several

advantages, like processing a large amount

of data and providing relevant information.

They make the diagnosis process more

accessible and more straightforward.

Pubrica guides you to make use of the

technologies wisely in this fast-emerging

world.

REFERENCES

1. Abdulaziz Mohsen, A., Alsurori, M., Aldobai, B.,

& Mohsen, G. A. (2019). A new approach to

medical diagnosis using artificial neural network

and decision tree algorithm: application to dental

diseases. International Journal of Information

Engineering and Electronic Business, 10(4), 52.

2. Amato, F., López, A., Peña-Méndez, E. M.,

Vaňhara, P., Hampl, A., & Havel, J. (2013).

Artificial neural networks in medical diagnosis.