Showing 2 results for Colon
M Asghari Jafarabadi, E Hajizadeh, A Kazemnejad, Sr Fatemi,
Volume 6, Issue 3 (12-2010)
Abstract
Background & Objectives: Cholera is always being considered as a public health threat in poor and developing countries. However outbreaks of cholera are not very common in central area of Iran in 2008 district health authority reported a cluster of diarrhea cases. We investigated this cluster to identify the etiological agent, source of transmission and propose control measures.
Methods: We analyzed the data of total of 1219 patients with colorectal cancer who registered between 1 January 2002 to 1 October 2007. Data were analyzed using univariate and multivariate Accelerated Failure Time (AFT) parametric survival model with frailty, utilizing STATA statistical software.
Results: In the univariate analysis for age at diagnosis, gender, marital status, race and education level, the survival of patients with colon cancer were approximately between half to one fourth and for BMI, alcohol history, Inflammatory Bowel Disease (IBD), familial history of cancer and the pathologic stage of tumor, the survival of patients with colon cancer were significantly (between 0.12 to 0.56 times) shorter than those patients with rectal cancer. In the multivariate analysis, for age at diagnosis (45-65 years), there was significant difference between colon and rectum cancer. But for BMI, alcohol history, IBD and pathologic stage there were not significant differences. The adjusted survival and 1, 2, 3, 4 and 5 year survival of patients with rectal cancer were better than those with colon cancer.
Conclusions: Site-specific evaluation of colon and rectum could give a better perspective of factors affecting these cancers. It may help to design of clinical trials, better diagnosis of diseases and optimal administration of specific treatments.
S Setareh, M Zahiri Esfahani , M Zare Bandamiri , A Raeesi, R Abbasi,
Volume 14, Issue 1 (6-2018)
Abstract
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.
