Investigating the Role of Feature Variation and Data Transformations of Different Types of Machine Learning Algorithms in Classifying Benign - Malignant Breast Cancer
Abstract
Objective :
to
explain
how
the
role
of
data
transformation
and
feature
selection
can
be
used
improve
performance
machine
learning
in
terms
classifying
breast
tumors
into
benign
or
malignant
categories
based
on
available
cancer
datasets.
Method :
taken
from
Kaggle
Wisconsin,
there
are
569
data,
consisting
357
benign,
212
malignant.
70%
is
for
training
30%
testing.
Data
divided
3
types
features
(10
features,
30
optional
features),
each
done
(original,
binary
bipolar).
By
using
7
algorithms
(logistic
regression,
decision
tree,
naïve
bayes,
random
forest,
SVM,
ANN,
KNN),
values
TP,
FP,
FN,
TN,
accuracy,
sensitivity,
specificity,
precision
calculated.
Results :
ANN
method
with
bipolar
has
highest
values.
Conclusion :
Proper
learning,
as
well
use
learning.
Research Square (Research Square), Год журнала: 2025, Номер unknown
Опубликована: Май 5, 2025
Язык: Английский