Algorithms in machine learning are a very important part because the type of algorithm used has an impact on the level of prediction accuracy and classification of a data set that is used. Appropriate use is accompanied by machine learning capabilities, namely being able to study past patterns, making machine learning have an advantage in prediction accuracy which can reach up to 90%. Therefore, machine learning has the opportunity to be an alternative that can avoid diagnostic errors that occur in the case of breast cancer. Breast cancer is one of the highlights of the impact of diagnostic errors because there are 10-30% of cases due to diagnostic errors, thus we need an alternative that can help reduce these diagnostic errors. In this study, an analysis of the logistic regression algorithm was carried out using the python programming language. The evaluation method is very important to know the performance in the prediction process. By using three evaluation methods, namely cross-validation k=10, confusion matrix, and ROC AUC. From the results of this study, it was found that the algorithm Logistic regression has an accuracy of 96.5% and an error of 0.19.

1.
Alpaydin
,
E.
(
2014
).
Introduction to machine learning, 3rd edition.
Cambridge, London
:
The MIT Press
.
2.
Azmi
,
Z.
, &
Dahria
,
M.
(
2013
).
Decision Tree Berbasis Algoritma Untuk Pengambilan Keputusan
.
Saintikom
,
12
,
157
164
.
3.
Bazazeh
,
D.
, &
Shubair
,
R.
(
2016
).
Comparative study of machine learning algorithms for breast cancer detection and diagnosis. In
2016 5th international conference on electronic devices, systems and applications (ICEDSA)
(pp.
1
4
).
IEEE
4.
Berrar
,
D.
(
2018
).
Cross-validation
.
Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics
,
1–3
(
January 2018
),
542
545
.
5.
Bharati
,
S.
,
Rahman
,
M. A.
, &
Podder
,
P.
(
2019
).
Breast cancer prediction applying different classification algorithm with comparative analysis using WEKA
.
4th International Conference on Electrical Engineering and Information and Communication Technology, ICEEiCT 2018
,
581
584
.
6.
Chernew
,
M. E.
, &
Landrum
,
M. B.
(
2018
).
Targeted Supplemental Data Collection — Addressing the Quality-Measurement Conundrum
.
New England Journal of Medicine
,
378
(
11
),
979
981
.
7.
Fenner
,
M.
(
2019
).
Machine learning with Python for everyone.
Addison-Wesley Professional
.
8.
Graber
,
M. L.
(
2013
).
The incidence of diagnostic error in medicine
.
BMJ Quality and Safety
,
22
(
SUPPL.2
),
21
28
.
9.
Gunasegaran
,
T.
, &
Cheah
,
Y. N.
(
2017
).
Evolutionary cross validation
.
ICIT 2017 - 8th International Conference on Information Technology, Proceedings
,
89
95
.
10.
Gupta
,
P.
, &
Garg
,
S.
(
2020
).
Breast Cancer Prediction using varying Parameters of Machine Learning Models
.
Procedia Computer Science
,
171
,
593
601
.
11.
Harlan
,
J.
(
2013
).
Analisis Regresi Logistik
. In
Journal of Chemical Information and Modeling
(Vol.
53
, Issue
9
).
12.
Johra
,
F. T.
, &
Shuvo
,
M. M. H.
(
2017
).
Detection of breast cancer from histopathology image and classifying benign and malignant state using fuzzy logic
.
2016 3rd International Conference on Electrical Engineering and Information and Communication Technology, ICEEiCT 2016
,
1
,
2
6
.
13.
Khorshid
,
S. F.
, &
Abdulazeez
,
A. M.
(
2021
).
Breast Cancer Diagnosis Based on K-Nearest Neighbors: a Review
.
PalArch's Journal of Archaeology of Egypt/Egyptology
,
18
(
4
),
1927
1951
.
14.
Kiyan
,
T.
, &
Yildirim
,
T.
(
2004
).
Breast cancer diagnosis using statistical neural networks
.
IU-Journal of Electrical & Electronics Engineering
,
4
(
2
),
1149
1153
.
15.
Murtirawat
,
R.
,
Panchal
,
S.
,
Singh
,
V. K.
, &
Panchal
,
Y.
(
2020
).
Breast Cancer Detection Using K-Nearest Neighbors, Logistic Regression and Ensemble Learning
.
Proceedings of the International Conference on Electronics and Sustainable Communication Systems, ICESC 2020, Icesc
,
534
540
.
16.
Narkhede
,
S.
(
2019
).
Understanding AUC - ROC Curve.
6
11
.
17.
Panch
,
T.
,
Szolovits
,
P.
, &
Atun
,
R.
(
2018
).
Artificial intelligence, machine learning and health systems
.
Journal of Global Health
,
8
(
2
),
1
8
.
18.
Ramadhan
,
H. A.
, &
Putri
,
D. A.
(
2018
).
Big Data, Kecerdasan Buatan, Blockchain, dan Teknologi Finansial di Indonesia
.
Direktorat Jenderal Aplikasi Informatika Kementerian Komunikasi Dan Informatika
,
1
66
.
19.
Rathi
,
M.
, &
Gupta
,
C.
(
2014
).
An approach to predict breast cancer and drug suggestion using machine learning techniques
.
ACEEE Int. J. on Information Technology
,
4
(
1
),
23
31
.
20.
Sauer
,
A. G.
,
Jemal
,
A.
,
Siegel
,
R. L.
, &
Miller
,
K. D.
(
2019
).
Breast Cancer Statistics, 2019.
0
(
0
),
1
14
.
21.
Sharma
,
R. K.
, &
Nair
,
A. R.
(
2019
).
Efficient breast cancer prediction using ensemble machine learning models. In
2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)
(pp.
100
104
).
IEEE
22.
Suprayogi
,
I.
,
Trimaijon
, &
Mahyudin
. (
2014
).
Model Prediksi Liku Kalibrasi Menggunakan Pendekatan Jaringan Saraf Tiruan (ZST) (Studi Kasus : Sub DAS Siak Hulu
).
Jurnal Online Mahasiswa Fakultas Teknik Universitas Riau
,
1
(
1
),
1
18
.
23.
Thirumalai
,
C.
(
2017
).
Method for Breast Cancer Type I Skin.
264
268
.
24.
Zhu
,
W.
,
Zeng
,
N.
, &
Wang
,
N.
(
2010
).
Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS ® Implementations K & L consulting services, Inc, Fort Washington, PA Octagon Research Solutions, Wayne
.
NESUG : Health Care and Life Sciences
,
1
9
.
This content is only available via PDF.
You do not currently have access to this content.