دورية أكاديمية
Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer
| العنوان: | Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer |
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| المؤلفون: | Mohammad Reza Afrash, Mostafa Shanbehzadeh, Hadi Kazemi-Arpanahi |
| المصدر: | Clinical Medicine Insights: Oncology, Vol 16 (2022) |
| بيانات النشر: | SAGE Publishing, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| مصطلحات موضوعية: | Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1179-5549 11795549 |
| Relation: | https://doaj.org/toc/1179-5549 |
| DOI: | 10.1177/11795549221116833 |
| URL الوصول: | https://doaj.org/article/27472a605ca84291aebbb705b3c80151 |
| رقم الأكسشن: | edsdoj.27472a605ca84291aebbb705b3c80151 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 11795549 |
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| DOI: | 10.1177/11795549221116833 |