research-article

Machine learning models in breast cancer survival prediction

Authors: Mitra Montazeri, Mohadeseh Montazeri, Mahdieh Montazeri, Amin BeigzadehAuthors Info & Claims

Technology and Health Care, Volume 24, Issue 1

Pages 31 - 42

Published: 27 January 2016 Publication History

Abstract

Background:

Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets.

Objective:

The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival.

Methods:

We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve.

Results:

Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%).

Conclusions:

This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for breast cancer survival prediction as well as medical decision making.

Cited By

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Göndöcs DDörfler V(2024)AI in medical diagnosisArtificial Intelligence in Medicine10.1016/j.artmed.2024.102769149:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.artmed.2024.102769
Xu LGuo CLiu M(2024)A weighted distance-based dynamic ensemble regression framework for gastric cancer survival time predictionArtificial Intelligence in Medicine10.1016/j.artmed.2023.102740147:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.artmed.2023.102740
Prusty SDas PDash SPatnaik SPrusty S(2023)Prediction of Breast cancer using integrated machine learning-fuzzy and dimension reduction techniquesJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22326545:1(1633-1652)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-223265
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Machine learning models in breast cancer survival prediction

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Information

Published In

Technology and Health Care Volume 24, Issue 1

Jan 2016

141 pages

ISSN:0928-7329

Issue’s Table of Contents

Publisher

IOS Press

Netherlands

Publication History

Published: 27 January 2016

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Göndöcs DDörfler V(2024)AI in medical diagnosisArtificial Intelligence in Medicine10.1016/j.artmed.2024.102769149:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.artmed.2024.102769
Xu LGuo CLiu M(2024)A weighted distance-based dynamic ensemble regression framework for gastric cancer survival time predictionArtificial Intelligence in Medicine10.1016/j.artmed.2023.102740147:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.artmed.2023.102740
Prusty SDas PDash SPatnaik SPrusty S(2023)Prediction of Breast cancer using integrated machine learning-fuzzy and dimension reduction techniquesJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22326545:1(1633-1652)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-223265
Susič DSyed-Abdul SDovgan EJonnagaddala JGradišek A(2023)Artificial intelligence based personalized predictive survival among colorectal cancer patientsComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2023.107435231:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.cmpb.2023.107435
Ahmad SUllah TAhmad IAL-Sharabi AUllah KKhan RRasheed SUllah IUddin MAli M(2022)A Novel Hybrid Deep Learning Model for Metastatic Cancer DetectionComputational Intelligence and Neuroscience10.1155/2022/81415302022Online publication date: 24-Jun-2022
https://dl.acm.org/doi/10.1155/2022/8141530
Kaur IDoja MAhmad T(2022)Data mining and machine learning in cancer survival researchJournal of Biomedical Informatics10.1016/j.jbi.2022.104026128:COnline publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.jbi.2022.104026
Shehab MAbualigah LShambour QAbu-Hashem MShambour MAlsalibi AGandomi A(2022)Machine learning in medical applicationsComputers in Biology and Medicine10.1016/j.compbiomed.2022.105458145:COnline publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1016/j.compbiomed.2022.105458
Kaur GGupta RHooda NGupta N(2022)Machine Learning Techniques and Breast Cancer Prediction: A ReviewWireless Personal Communications: An International Journal10.1007/s11277-022-09673-3125:3(2537-2564)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s11277-022-09673-3
Alrefai NIbrahim O(2022)Optimized feature selection method using particle swarm intelligence with ensemble learning for cancer classification based on microarray datasetsNeural Computing and Applications10.1007/s00521-022-07147-y34:16(13513-13528)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s00521-022-07147-y
Khan AHe NTariq ILi Z(2021)Stacking Ensemble Method for Early and Advanced Stage Lung Adenocarcinoma Classification Based on miRNA ExpressionProceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science10.1145/3498731.3498742(76-81)Online publication date: 29-Oct-2021
https://dl.acm.org/doi/10.1145/3498731.3498742
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Abstract

Background:

Objective:

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Results:

Conclusions:

Cited By

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Prediction of Breast Cancer using Machine Learning Techniques

Machine Learning Techniques for Survival Time Prediction in Breast Cancer

Machine Learning Techniques for Breast Cancer Prediction

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