Artificial intelligence,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 19, 2024
Breast
cancer
is
a
significant
transnational
health
concern,
requiring
effective
timely
detection
methods
to
improve
patient’s
treatment
result
and
reduce
mortality
rates.
While
conventional
screening
like
mammography,
ultrasound,
MRI
have
proven
efficacy,
they
possess
limitations,
such
as
false-positive
results
discomfort.
In
recent
years,
machine
learning
(ML)
deep
(DL)
techniques
demonstrated
potential
in
transforming
breast
through
the
analysis
of
imaging
data.
This
review
systematically
explores
advancements
research
applications
for
detecting
cancer.
Through
systematic
existing
literature,
we
identify
trends,
challenges,
opportunities
development
deployment
ML
DL
models
diagnosis.
We
highlight
crucial
role
early
enhancing
patient
outcomes
lowering
Furthermore,
impact
technologies
on
clinical
procedure,
outcomes,
healthcare
delivery
detection.
By
identifying
evaluating
studies
detection,
aim
provide
valuable
insights
researchers,
clinicians,
policymakers,
stakeholders
interested
leveraging
advanced
computational
enhance
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 30, 2025
Abstract
The
classification
of
chronic
diseases
has
long
been
a
prominent
research
focus
in
the
field
public
health,
with
widespread
application
machine
learning
algorithms.
Diabetes
is
one
high
prevalence
worldwide
and
considered
disease
its
own
right.
Given
nature
this
condition,
numerous
researchers
are
striving
to
develop
robust
algorithms
for
accurate
classification.
This
study
introduces
revolutionary
approach
accurately
classifying
diabetes,
aiming
provide
new
methodologies.
An
improved
Secretary
Bird
Optimization
Algorithm
(QHSBOA)
proposed
combination
Kernel
Extreme
Learning
Machine
(KELM)
diabetes
prediction
model.
First,
(SBOA)
enhanced
by
integrating
particle
swarm
optimization
search
mechanism,
dynamic
boundary
adjustments
based
on
optimal
individuals,
quantum
computing-based
t-distribution
variations.
performance
QHSBOA
validated
using
CEC2017
benchmark
suite.
Subsequently,
used
optimize
kernel
penalty
parameter
$$\:C$$
bandwidth
$$\:c$$
KELM.
Comparative
experiments
other
models
conducted
datasets.
experimental
results
indicate
that
QHSBOA-KELM
model
outperforms
comparative
four
evaluation
metrics:
accuracy
(ACC),
Matthews
correlation
coefficient
(MCC),
sensitivity,
specificity.
offers
an
effective
method
early
diagnosis
diabetes.
Diagnostics,
Год журнала:
2024,
Номер
14(23), С. 2632 - 2632
Опубликована: Ноя. 22, 2024
Accurate
classification
in
cancer
research
is
vital
for
devising
effective
treatment
strategies.
Precise
depends
significantly
on
selecting
the
most
informative
genes
from
high-dimensional
datasets,
a
task
made
complex
by
extensive
data
involved.
This
study
introduces
Two-stage
MI-PSA
Gene
Selection
algorithm,
novel
approach
designed
to
enhance
accuracy
through
robust
gene
selection
methods.
This
study
proposes
an
advanced
machine
learning
(ML)
framework
for
breast
cancer
diagnostics
by
integrating
transcriptomic
profiling
with
optimized
feature
selection
and
classification
techniques.
A
dataset
of
1759
samples
(987
patients,
772
healthy
controls)
was
analyzed
using
Recursive
Feature
Elimination,
Boruta,
ElasticNet
selection.
Dimensionality
reduction
techniques,
including
Non-Negative
Matrix
Factorization
(NMF),
Autoencoders,
transformer-based
embeddings
(BioBERT,
DNABERT),
were
applied
to
enhance
model
interpretability.
Classifiers
such
as
XGBoost,
LightGBM,
ensemble
voting,
Multi-Layer
Perceptron,
Stacking
trained
grid
search
cross-validation.
Model
evaluation
conducted
accuracy,
AUC,
MCC,
Kappa
Score,
ROC,
PR
curves,
external
validation
performed
on
independent
175
samples.
XGBoost
LightGBM
achieved
the
highest
test
accuracies
(0.91
0.90)
AUC
values
(up
0.92),
particularly
NMF
BioBERT.
The
Voting
method
exhibited
best
accuracy
(0.92),
confirming
its
robustness.
Transformer-based
techniques
significantly
improved
performance
compared
conventional
approaches
like
PCA
Decision
Trees.
proposed
ML
enhances
diagnostic
interpretability,
demonstrating
strong
generalizability
dataset.
These
findings
highlight
potential
precision
oncology
personalized
diagnostics.
BMC Medical Informatics and Decision Making,
Год журнала:
2025,
Номер
25(1)
Опубликована: Фев. 7, 2025
The
relevance
of
the
study
is
due
to
growing
number
diseases
cerebrovascular
system,
in
particular
stroke,
which
one
leading
causes
disability
and
mortality
world.
To
improve
stroke
risk
prediction
models
terms
efficiency
interpretability,
we
propose
integrate
modern
machine
learning
algorithms
data
dimensionality
reduction
methods,
XGBoost
optimized
principal
component
analysis
(PCA),
provide
structuring
increase
processing
speed,
especially
for
large
datasets.
For
first
time,
explainable
artificial
intelligence
(XAI)
integrated
into
PCA
process,
increases
transparency
interpretation,
providing
a
better
understanding
factors
medical
professionals.
proposed
approach
was
tested
on
two
datasets,
with
accuracy
95%
98%.
Cross-validation
yielded
an
average
value
0.99,
high
values
Matthew's
correlation
coefficient
(MCC)
metrics
0.96
Cohen's
Kappa
(CK)
confirmed
generalizability
reliability
model.
speed
increased
threefold
OpenMP
parallelization,
makes
it
possible
apply
practice.
Thus,
method
innovative
can
potentially
forecasting
systems
healthcare
industry.
