Comparing the Performance of Feature Selection Methods for Predicting Gastric Cancer.
Background: Gastric cancer (GC) is a leading cause of cancer-related deaths, emphasizing the importance of timely diagnosis for effective treatment. Machine learning models have shown promise in assisting with GC diagnosis. Objectives: This study aimed at comparing the performance of various feature...
| Published in: | International Journal of Cancer Management Vol. 16; no. 1; pp. 1 - 11 |
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| Main Authors: | , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Medical Journals Commission of the Ministry of Health & Medical Education
Dec2023
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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=174742657&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174742657 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 25384422 L6AI jtl: International Journal of Cancer Management issn: 25384422 maglogo: N pubinfo: dt: Dec2023 vid: 16 iid: 1 pid: 66482 pub: Medical Journals Commission of the Ministry of Health & Medical Education artinfo: ui: 174742657 174742657 174742657 10.5812/ijcm-138653 174742657 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Comparing the Performance of Feature Selection Methods for Predicting Gastric Cancer. aug: au: Mazreati, Hamed Radfar, Reza Sohrabi, Mohammad-Reza Divshali, Babak Sabet Afshar Kazemi, Mohammad Ali affil: Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran sug: subj: Stomach Neoplasms Diagnosis Stomach Neoplasms Therapy Life Style Machine Learning Methods Artificial Intelligence Methods Prediction Models Methods Models, Theoretical Early Detection of Cancer Methods Goals and Objectives Cancer Patients Human Female Iran Evaluation Research Decision Trees Algorithms Random Forest Early Diagnosis Methods Risk Assessment Data Mining Methods Descriptive Statistics Comparative Studies ROC Curve Female ab: Background: Gastric cancer (GC) is a leading cause of cancer-related deaths, emphasizing the importance of timely diagnosis for effective treatment. Machine learning models have shown promise in assisting with GC diagnosis. Objectives: This study aimed at comparing the performance of various feature selection methods in identifying influential factors related to GC based on lifestyle using machine learning models. The ultimate goal was to enhance early detection and treatment of the disease. Methods: The data of patients from Shahid Ayatollah Modarres Hospital and Shohadaye Tajrish Hospital between 2013 and 2021 were utilized. Three feature selection methods (filter, wrapper, and filter-wrapper) were employed. The k-fold method validated each model. Four classifiers k Nearest Neighbor (kNN), Decision Tree (DT), Random Forest (RF), and Gradient-Boosted Decision Trees (GBDT) compared their outputs based on feature selection methods. Results: The filter-wrapper method outperformed others, achieving an area under the ROC curve and Fl score of 95.8% and 94.7%, respectively. GBDT also performed well. The wrapper and RF classifiers achieved an area under the ROC curve and Fl scores of 95.7% and 93.6%, respectively, after the filter-wrapper method. Without feature selection methods, the RF classifier had an area under the ROC curve and F1 scores of 95.6% and 91.7%, respectively, surpassing other classifiers. Conclusions: This study suggests that appropriate feature selection methods for identifying influential factors related to GC based on lifestyle can facilitate early diagnosis and treatment. The filter-wrapper method demonstrated the best performance in this regard. 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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