PMID- 35509861
OWN - NLM
STAT- MEDLINE
DCOM- 20220506
LR  - 20230707
IS  - 1748-6718 (Electronic)
IS  - 1748-670X (Print)
IS  - 1748-670X (Linking)
VI  - 2022
DP  - 2022
TI  - Predicting Characteristics Associated with Breast Cancer Survival Using Multiple 
      Machine Learning Approaches.
PG  - 1249692
LID - 10.1155/2022/1249692 [doi]
LID - 1249692
AB  - Breast cancer is one of the most commonly diagnosed female disorders globally. 
      Numerous studies have been conducted to predict survival markers, although the 
      majority of these analyses were conducted using simple statistical techniques. In 
      lieu of that, this research employed machine learning approaches to develop 
      models for identifying and visualizing relevant prognostic indications of breast 
      cancer survival rates. A comprehensive hospital-based breast cancer dataset was 
      collected from the National Cancer Institute's SEER Program's November 2017 
      update, which offers population-based cancer statistics. The dataset included 
      female patients diagnosed between 2006 and 2010 with infiltrating duct and 
      lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 
      8522/3). The dataset included nine predictor factors and one predictor variable 
      that were linked to the patients' survival status (alive or dead). To identify 
      important prognostic markers associated with breast cancer survival rates, 
      prediction models were constructed using K-nearest neighbor (K-NN), decision tree 
      (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression 
      (LR), voting classifier, and support vector machine (SVM). All methods yielded 
      close results in terms of model accuracy and calibration measures, with the 
      lowest achieved from logistic regression (accuracy = 80.57 percent) and the 
      greatest acquired from the random forest (accuracy = 94.64 percent). Notably, the 
      multiple machine learning algorithms utilized in this research achieved high 
      accuracy, suggesting that these approaches might be used as alternative 
      prognostic tools in breast cancer survival studies, especially in the Asian area.
CI  - Copyright (c) 2022 Mohammad Nazmul Haque et al.
FAU - Haque, Mohammad Nazmul
AU  - Haque MN
AD  - Department of Electrical and Computer Engineering, North South University, 
      Bashundhara, Dhaka 1229, Bangladesh.
FAU - Tazin, Tahia
AU  - Tazin T
AUID- ORCID: 0000-0002-6335-3802
AD  - Department of Electrical and Computer Engineering, North South University, 
      Bashundhara, Dhaka 1229, Bangladesh.
FAU - Khan, Mohammad Monirujjaman
AU  - Khan MM
AUID- ORCID: 0000-0003-0779-8820
AD  - Department of Electrical and Computer Engineering, North South University, 
      Bashundhara, Dhaka 1229, Bangladesh.
FAU - Faisal, Shahla
AU  - Faisal S
AUID- ORCID: 0000-0002-6303-5986
AD  - Department of Statistics, Government College University, Faisalabad, Pakistan.
FAU - Ibraheem, Sobhee Md
AU  - Ibraheem SM
AD  - Department of Electrical and Computer Engineering, North South University, 
      Bashundhara, Dhaka 1229, Bangladesh.
FAU - Algethami, Haneen
AU  - Algethami H
AUID- ORCID: 0000-0002-7582-4480
AD  - Department of Computer Science, College of Computers and Information Technology, 
      Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
FAU - Almalki, Faris A
AU  - Almalki FA
AUID- ORCID: 0000-0002-1291-055X
AD  - Department of Computer Engineering, College of Computers and Information 
      Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
LA  - eng
PT  - Journal Article
PT  - Retracted Publication
DEP - 20220425
PL  - United States
TA  - Comput Math Methods Med
JT  - Computational and mathematical methods in medicine
JID - 101277751
SB  - IM
RIN - Comput Math Methods Med. 2023 Jun 28;2023:9832573. PMID: 37416292
MH  - *Breast Neoplasms/diagnosis
MH  - Female
MH  - Humans
MH  - Logistic Models
MH  - Machine Learning
MH  - Prognosis
MH  - Support Vector Machine
PMC - PMC9060999
COIS- The authors declare that they have no conflicts of interest to report regarding 
      the present study.
EDAT- 2022/05/06 06:00
MHDA- 2022/05/07 06:00
PMCR- 2022/04/25
CRDT- 2022/05/05 02:31
PHST- 2022/01/28 00:00 [received]
PHST- 2022/03/29 00:00 [accepted]
PHST- 2022/05/05 02:31 [entrez]
PHST- 2022/05/06 06:00 [pubmed]
PHST- 2022/05/07 06:00 [medline]
PHST- 2022/04/25 00:00 [pmc-release]
AID - 10.1155/2022/1249692 [doi]
PST - epublish
SO  - Comput Math Methods Med. 2022 Apr 25;2022:1249692. doi: 10.1155/2022/1249692. 
      eCollection 2022.