Journal of Health Innovation and Environmental Education.,
Journal Year:
2024,
Volume and Issue:
1(2), P. 53 - 59
Published: Dec. 31, 2024
Purpose
of
the
study:
The
purpose
this
study
was
to
determine
relationship
between
knowledge
and
attitudes
with
BSE
behavior
in
students
Public
Health
Study
Program,
Jambi
University.
Methodology:
This
used
a
descriptive
analytic
research
design
cross
sectional
approach.
sampling
technique
multistage
random
on
307
by
filling
an
online
questionnaire
through
Googleform.
variables
were
knowledge,
which
analyzed
using
Chi-square
test.
Main
Findings:
Knowledge
female
good
category
is
73
people.
Attitudes
positive
are
52
people,
for
68
There
no
significant
behavior,
there
behavior.
Novelty/Originality
results
expected
be
useful
as
material
developing
scientific
add
literature
breast
cancer
itself
well
policies
regarding
prevention
non-communicable
diseases,
especially
students.
JAMIA Open,
Journal Year:
2024,
Volume and Issue:
7(3)
Published: July 1, 2024
This
study
uses
electronic
health
record
(EHR)
data
to
predict
12
common
cancer
symptoms,
assessing
the
efficacy
of
machine
learning
(ML)
models
in
identifying
symptom
influencers.
Biomimetics,
Journal Year:
2024,
Volume and Issue:
9(10), P. 609 - 609
Published: Oct. 9, 2024
Breast
cancer
remains
a
global
health
problem
requiring
effective
diagnostic
methods
for
early
detection,
in
order
to
achieve
the
World
Health
Organization’s
ultimate
goal
of
breast
self-examination.
A
literature
review
indicates
urgency
improving
and
identifies
thermography
as
promising,
cost-effective,
non-invasive,
adjunctive,
complementary
detection
method.
This
research
explores
potential
using
machine
learning
techniques,
specifically
Bayesian
networks
combined
with
convolutional
neural
networks,
improve
possible
diagnosis
at
stages.
Explainable
artificial
intelligence
aims
clarify
reasoning
behind
any
output
network-based
models.
The
proposed
integration
adds
interpretability
diagnosis,
which
is
particularly
significant
medical
diagnosis.
We
constructed
two
expert
models:
Model
B.
In
this
research,
A,
combining
thermal
images
after
explainable
process
together
records,
achieved
an
accuracy
84.07%,
while
model
B,
also
includes
network
prediction,
90.93%.
These
results
demonstrate
very
high
accuracy.
Mathematics,
Journal Year:
2024,
Volume and Issue:
12(18), P. 2808 - 2808
Published: Sept. 11, 2024
Breast
cancer
is
one
of
the
most
lethal
and
widespread
diseases
affecting
women
worldwide.
As
a
result,
it
necessary
to
diagnose
breast
accurately
efficiently
utilizing
cost-effective
widely
used
methods.
In
this
research,
we
demonstrated
that
synthetically
created
high-quality
ultrasound
data
outperformed
conventional
augmentation
strategies
for
diagnosing
using
deep
learning.
We
trained
deep-learning
model
EfficientNet-B7
architecture
large
dataset
3186
images
acquired
from
multiple
publicly
available
sources,
as
well
10,000
generated
generative
adversarial
networks
(StyleGAN3).
The
was
five-fold
cross-validation
techniques
validated
four
metrics:
accuracy,
recall,
precision,
F1
score
measure.
results
showed
integrating
produced
into
training
set
increased
classification
accuracy
88.72%
92.01%
based
on
score,
demonstrating
power
models
expand
improve
quality
datasets
in
medical-imaging
applications.
This
larger
comprising
synthetic
significantly
improved
its
performance
by
more
than
3%
over
genuine
with
common
augmentation.
Various
procedures
were
also
investigated
set’s
diversity
representativeness.
research
emphasizes
relevance
modern
artificial
intelligence
machine-learning
technologies
medical
imaging
providing
an
effective
strategy
categorizing
images,
which
may
lead
diagnostic
optimal
treatment
options.
proposed
are
highly
promising
have
strong
potential
future
clinical
application
diagnosis
cancer.
Accurate
lung
cancer
classification
is
important
for
patient
treatment.
However,
existing
methods
inefficiently
classify
cancer.
Therefore,
the
binary
count
ratio
(BCR)
proposed
to
enhance
accuracy
of
classification.
This
method
utilizes
adaptive
thresholding
based
on
column
mean
binarize
CT
images.
The
features
BCR
are
computed
by
using
black
and
white
pixels
identify
areas.
captures
which
areas
in
scan
After
that,
Euclidean
distance
used
a
normal
or
benign
malignant
class.
For
1,097
images
IQ-OTH/NCCD
dataset,
achieves
0.9810,
0.9618,
0.9705,
0.9832
precision,
recall,
F1-score,
values
higher
than
conventional
methods.
able
efficiently
extract
pixel
cancer,
thus
improving
effectiveness
