Using Data Mining for Survival Prediction in Patients with Colon Cancer
Background and Objectives: Colon cancer is the third most common cancer in the world and the fourth most common cancer in Iran. It is very important to predict the cancer outcome and its basic clinical data. Due to to the high rate of colon cancer and the benefits of data mining to predict survival,...
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| Format: | Article |
| Language: | fas |
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Tehran University of Medical Sciences
2018-06-01
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| Series: | مجله اپیدمیولوژی ایران |
| Subjects: | |
| Online Access: | http://irje.tums.ac.ir/article-1-5961-en.html |
| _version_ | 1819291072950435840 |
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| author | S Setareh M Zahiri Esfahani M Zare Bandamiri A Raeesi R Abbasi |
| author_facet | S Setareh M Zahiri Esfahani M Zare Bandamiri A Raeesi R Abbasi |
| author_sort | S Setareh |
| collection | DOAJ |
| description | Background and Objectives: Colon cancer is the third most common cancer in the world and the fourth most common cancer in Iran. It is very important to predict the cancer outcome and its basic clinical data. Due to to the high rate of colon cancer and the benefits of data mining to predict survival, the aim of this study was to survey two widely used machine learning algorithms, Bagging and Support Vector Machines (SVM), to predict the outcome of colon cancer patients.
Methods: The population of this study was 567 patients with stage 1-4 of colon cancer in Namazi Radiotherapy Center, Shiraz in 2006-2011. Three hundred and thirty eight patients were alive and 229 patients were dead. We used the Support Vector Machines (SVM) and Bagging methods in order to predict the survival of patients with colon cancer. The Weka software ver 3.6.10 was used for data analysis.
Results: The performance of two algorithms was determined using the confusion matrix. The accuracy, specificity, and sensitivity of the SVM was 84.48%, 81%, and 87%, and the accuracy, specificity, and sensitivity of Bagging was 83.95%, 78%, and 88%, respectively.
Conclusion: The results showed both algorithms have a high performance in survival prediction of patients with colon cancer but the Support Vector Machines has a higher accuracy. |
| first_indexed | 2024-12-24T03:32:50Z |
| format | Article |
| id | doaj.art-cda5ff66ea764357be2abc1d2e194ed7 |
| institution | Directory Open Access Journal |
| issn | 1735-7489 2228-7507 |
| language | fas |
| last_indexed | 2024-12-24T03:32:50Z |
| publishDate | 2018-06-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | مجله اپیدمیولوژی ایران |
| spelling | doaj.art-cda5ff66ea764357be2abc1d2e194ed72022-12-21T17:17:09ZfasTehran University of Medical Sciencesمجله اپیدمیولوژی ایران1735-74892228-75072018-06-011411929Using Data Mining for Survival Prediction in Patients with Colon CancerS Setareh0M Zahiri Esfahani1M Zare Bandamiri2A Raeesi3R Abbasi4 PhD Student of Medical Informatics, Faculty of Paramedicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran PhD Student of Health Information Management, Faculty of Management and Medical Information Sciences, Iran University of Medical Sciences, Tehran, Iran Department of Radiation Oncology, Namazi hospital, Shiraz University Medical Sciences, Shiraz, Iran MSc Student of Medical Informatics, Medical Informatics Research Center, Institute of Future Study, Kerman University of Medical Sciences, Kerman, Iran PhD Student of Health Information Management, Health Informatin Management Research Center, Kashan University of Medical Sciencess, Kashan, Iran;Researcher of Medical Informatics Research Center (MIRC), Institute of Future Study, Kerman University of Medical Sciences, Kerman, Iran Background and Objectives: Colon cancer is the third most common cancer in the world and the fourth most common cancer in Iran. It is very important to predict the cancer outcome and its basic clinical data. Due to to the high rate of colon cancer and the benefits of data mining to predict survival, the aim of this study was to survey two widely used machine learning algorithms, Bagging and Support Vector Machines (SVM), to predict the outcome of colon cancer patients. Methods: The population of this study was 567 patients with stage 1-4 of colon cancer in Namazi Radiotherapy Center, Shiraz in 2006-2011. Three hundred and thirty eight patients were alive and 229 patients were dead. We used the Support Vector Machines (SVM) and Bagging methods in order to predict the survival of patients with colon cancer. The Weka software ver 3.6.10 was used for data analysis. Results: The performance of two algorithms was determined using the confusion matrix. The accuracy, specificity, and sensitivity of the SVM was 84.48%, 81%, and 87%, and the accuracy, specificity, and sensitivity of Bagging was 83.95%, 78%, and 88%, respectively. Conclusion: The results showed both algorithms have a high performance in survival prediction of patients with colon cancer but the Support Vector Machines has a higher accuracy.http://irje.tums.ac.ir/article-1-5961-en.htmlcolon cancersurvival predictiondata miningsupport vector machinesbagging |
| spellingShingle | S Setareh M Zahiri Esfahani M Zare Bandamiri A Raeesi R Abbasi Using Data Mining for Survival Prediction in Patients with Colon Cancer مجله اپیدمیولوژی ایران colon cancer survival prediction data mining support vector machines bagging |
| title | Using Data Mining for Survival Prediction in Patients with Colon Cancer |
| title_full | Using Data Mining for Survival Prediction in Patients with Colon Cancer |
| title_fullStr | Using Data Mining for Survival Prediction in Patients with Colon Cancer |
| title_full_unstemmed | Using Data Mining for Survival Prediction in Patients with Colon Cancer |
| title_short | Using Data Mining for Survival Prediction in Patients with Colon Cancer |
| title_sort | using data mining for survival prediction in patients with colon cancer |
| topic | colon cancer survival prediction data mining support vector machines bagging |
| url | http://irje.tums.ac.ir/article-1-5961-en.html |
| work_keys_str_mv | AT ssetareh usingdataminingforsurvivalpredictioninpatientswithcoloncancer AT mzahiriesfahani usingdataminingforsurvivalpredictioninpatientswithcoloncancer AT mzarebandamiri usingdataminingforsurvivalpredictioninpatientswithcoloncancer AT araeesi usingdataminingforsurvivalpredictioninpatientswithcoloncancer AT rabbasi usingdataminingforsurvivalpredictioninpatientswithcoloncancer |