Identification of Effective Factors in Breast Cancer Survival in Isfahan Using Machine Learning Techniques.
Background & Objective: Breast cancer is a leading cause of female mortalities worldwide. This study has used machine learning techniques to determine the most critical factors influencing the survival rate of breast cancer patients in Isfahan. Materials & Methods: A list of variables influencing th...
| Published in: | Journal of Advances in Medical & Biomedical Research Vol. 32; no. 154; pp. 350 - 361 |
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| Main Authors: | , , , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Zanjan University of Medical Sciences & Health Services
Sep/Oct2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182224073&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182224073 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26766264 N5MJ jtl: Journal of Advances in Medical & Biomedical Research issn: 26766264 maglogo: N pubinfo: dt: Sep/Oct2024 vid: 32 iid: 154 pid: 65276 pub: Zanjan University of Medical Sciences & Health Services artinfo: ui: 182224073 182224073 182224073 10.30699/jambs.32.154.350 182224073 ppf: 350 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Identification of Effective Factors in Breast Cancer Survival in Isfahan Using Machine Learning Techniques. aug: au: Bagherian, Hossein Javanmard, Shaghayegh Haghjooy Mosayebi, Azam Noorshargh, Pegah Arabzedeh, Saeedeh Sharifi, Mehran Sattari, Mohammad affil: Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. sug: subj: Breast Neoplasms Diagnosis Machine Learning Methods Survival Rate Cancer Patients Psychosocial Factors Data Mining Predictive Value of Tests Iran Human Female Lymph Node Excision Receptors, Estrogen Receptors, Progesterone HER-2-neu Oncogene Descriptive Statistics Data Analysis Software Linear Regression Survival Analysis Funding Source Female ab: Background & Objective: Breast cancer is a leading cause of female mortalities worldwide. This study has used machine learning techniques to determine the most critical factors influencing the survival rate of breast cancer patients in Isfahan. Materials & Methods: A list of variables influencing the survival of breast cancer patients was initially extracted from the data sets of two Isfahan hospitals for this analytical investigation, leading to the extraction of 16 critical factors based on the opinions of oncologists. In the next step, the missing values were identified and deleted or corrected, followed by converting some features into numerical ranges. Ultimately, the key variables influencing the survival rate of breast cancer patients were determined by applying 11 machine learning algorithms. Results: Forward selection is more accurate than other techniques. Of the 15 input features, 13 were extracted as influential survival rates at least once using different techniques, with BC-ER-PR-HER2 ranking first among the features. The six first features, including Bc-ER-PR-HER2, lymph node dissection, behavior, primary surgery procedure, the exact number of nodes examined, and the exact number of positive nodes, were determined as the best combination for identifying breast cancer patients. Even though cancer behavior patterns differ in various societies, there are still similarities in risk factors. Conclusion: Forward selection combined with principal component analysis using support vector machines, neural networks, and random forests can be the best model for breast cancer prediction. Neural networks, random forests, and support vector machines are very good at predicting breast cancer survival. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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