Survival Prediction of Patients with Breast Cancer: Comparisons of Decision Tree and Logistic Regression Analysis

Momenyan, Somayeh; Baghestani, Ahmad Reza; Momenyan, Narges; Naseri, Parisa; Akbari, Mohammad Esmaeil

doi:10.5812/ijcm.9176

Cited by 15 publications

(13 citation statements)

References 29 publications

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“…This process was then repeated for every subsample and the mean of the 10 results was estimated as the misclassification risk value [ 30 ]. In addition, the accuracy of the DT and multiple logistic analyses was compared [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

A Risk Prediction Flowchart of Vancomycin-Induced Acute Kidney Injury to Use When Starting Vancomycin Administration: A Multicenter Retrospective Study

et al. 2020

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We previously constructed a risk prediction model of vancomycin (VCM)-associated nephrotoxicity for use when performing initial therapeutic drug monitoring (TDM), using decision tree analysis. However, we could not build a model to be used at the time of initial administration due to insufficient sample size. Therefore, we performed a multicenter study at four hospitals in Japan. We investigated patients who received VCM intravenously at a standard dose from the first day until the initial TDM from November 2011 to March 2019. Acute kidney injury (AKI) was defined according to the criteria established by the “Kidney disease: Improving global outcomes” group. We extracted potential risk factors that could be evaluated on the day of initial administration and constructed a flowchart using a chi-squared automatic interaction detection algorithm. Among 843 patients, 115 (13.6%) developed AKI. The flowchart comprised three splitting variables (concomitant drugs (vasopressor drugs and tazobactam/piperacillin) and body mass index ≥ 30) and four subgroups. The incidence rates of AKI ranged from 9.34 to 36.8%, and they were classified as low-, intermediate-, and high-risk groups. The accuracy of flowchart was judged appropriate (86.4%). We successfully constructed a simple flowchart predicting VCM-induced AKI to be used when starting VCM administration.

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Section: Methodsmentioning

confidence: 99%

A Risk Prediction Flowchart of Vancomycin-Induced Acute Kidney Injury to Use When Starting Vancomycin Administration: A Multicenter Retrospective Study

et al. 2020

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“…Studies have demonstrated the utility of the decision tree model in clinical applications. In a study on the prognosis of breast cancer patients, a decision tree model and a classical logistic regression model were constructed, respectively, with the predictive performance of the different models indicating that the decision tree model showed stronger predictive power when using real clinical data [ 38 ]. Similarly, the decision tree model has been applied to other areas of clinical medicine, including diagnosis of kidney stones [ 39 ], predicting the risk of sudden cardiac arrest [ 40 ], and exploration of the risk factors of type II diabetes [ 41 ].…”

Section: Data-mining Algorithms For Clinical Big Datamentioning

confidence: 99%

Data mining in clinical big data: the frequently used databases, steps, and methodological models

et al. 2021

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Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

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“…They tested three methods to establish the model namely Support Vector Machine, Logistic Regression and Decision Tree to find the best approach that will contribute as a reference in decision-making frame. In 2018, a group of different authors carried out the same method by comparing decision tree and logistic regression for survival prediction of breast cancer patients [58].…”

Section: Recurrence Of Breast Cancermentioning

confidence: 99%

“…The traditional way in ascertaining the prognostic factors related with the survival time of breast cancer patients is by doing statistical methods. The statistical analysis assists in determining the important predictors with regards to the outcome of patients' survivability or recurrence of time [58]. However, with the machine learning method, more accurate prognostic factors of breast cancer can be identified for more effective management of cancer.…”

Section: Categories Of Breast Cancer-related Researchmentioning

confidence: 99%

“…However, with the machine learning method, more accurate prognostic factors of breast cancer can be identified for more effective management of cancer. In a study by Momenyan et al [58], it is found that the use of decision tree is more accurate in predicting the probability of death due to breast cancer. Likewise, the adoption of decision tree in the cases of non-metastatic invasive breast cancer is found to be 70% accurate to predict the early relapse of the disease [81].…”

Section: Categories Of Breast Cancer-related Researchmentioning

confidence: 99%

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The use of Decision Tree in Breast Cancer-Related Research: a Scoping Analysis Based on Scopus-Indexed Articles

Badi’auzzaman¹,

Moey²,

Azemin³

et al. 2019

IJITEE

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Breast cancer is the leading cancer that occurs in women globally. The use of machine learning has been introduced to supplement the work in breast cancer studies. There are undisputed pieces of evidence of the existence of publications pertaining to the use of decision tree in breast cancer-related research. However, little is known regarding the types and frequencies of the searched articles. The main objective of this paper is to unearth the broad variety of articles related to breast cancer research that utilized decision trees. The Scopus database was chosen to examine the trend, frequencies and themes of the related publications from the year 2013 until 2018. The study was also intended to disclose the categories of articles based on the areas of breast cancer that have employed the decision trees method. A total of 259 articles from Scopus database were found to meet the inclusion criteria. The analysis of the frequency of published articles generally shows an upward trend. The majority of articles targeted diagnosis of breast cancer (37.8%) in comparisons with other categories. Even though the number of articles found is adequate, several categories of breast cancer are lacking in publications specifically the survivability, incidence, and recurrence of breast cancer among patients. There is a need to redirect the focus of breast cancer research on these categories for future efforts

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Survival Prediction of Patients with Breast Cancer: Comparisons of Decision Tree and Logistic Regression Analysis

Cited by 15 publications

References 29 publications

A Risk Prediction Flowchart of Vancomycin-Induced Acute Kidney Injury to Use When Starting Vancomycin Administration: A Multicenter Retrospective Study

A Risk Prediction Flowchart of Vancomycin-Induced Acute Kidney Injury to Use When Starting Vancomycin Administration: A Multicenter Retrospective Study

Data mining in clinical big data: the frequently used databases, steps, and methodological models

The use of Decision Tree in Breast Cancer-Related Research: a Scoping Analysis Based on Scopus-Indexed Articles

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