Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer.
Background: Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop...
| Publicado en: | Clinical Medicine Insights: Oncology pp. 1 - 14 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
8/22/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158668668&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158668668 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11795549 B3KT jtl: Clinical Medicine Insights: Oncology issn: 11795549 maglogo: Y pubinfo: dt: 8/22/2022 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 158668668 158668668 158668668 10.1177/11795549221116833 158668668 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer. aug: au: Afrash, Mohammad Reza Shanbehzadeh, Mostafa Kazemi-Arpanahi, Hadi affil: Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran sug: subj: Stomach Neoplasms Prognosis Cancer Patients Survival Analysis Evaluation Machine Learning Utilization Software Design Prediction Models Human Algorithms Retrospective Design Decision Trees Sensitivity and Specificity Data Mining Patient Safety Decision Making, Computer Assisted ab: Background: Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop an intelligent system based on machine learning (ML) algorithms for predicting the 5-year survival status in gastric cancer patients. Methods: A data set that includes the records of 974 gastric cancer patients retrospectively was used. First, the most important predictors were recognized using the Boruta feature selection algorithm. Five classifiers, including J48 decision tree (DT), support vector machine (SVM) with radial basic function (RBF) kernel, bootstrap aggregating (Bagging), hist gradient boosting (HGB), and adaptive boosting (AdaBoost), were trained for predicting gastric cancer survival. The performance of the used techniques was evaluated with specificity, sensitivity, likelihood ratio, and total accuracy. Finally, the system was developed according to the best model. Results: The stage, position, and size of tumor were selected as the 3 top predictors for gastric cancer survival. Among the 6 selected ML algorithms, the HGB classifier with the mean accuracy, mean specificity, mean sensitivity, mean area under the curve, and mean F1-score of 88.37%, 86.24%, 89.72%, 88.11%, and 89.91%, respectively, gained the best performance. Conclusions: The ML models can accurately predict the 5-year survival and potentially act as a customized recommender for decision-making in gastric cancer patients. The developed system in our study can improve the quality of treatment, patient safety, and survival rates; it may guide prescribing more personalized medicine. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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