Salmanpoursohi, Bita, Daneshvar, Amir, Salmanpoursohi, Shakiba, Pourghader Chobar, Adel, Salahi, Fariba. (1403). Cancer detection from textual data using a combination of machine learning approach. , 17(3), 1001-1014. doi: 10.22059/ijms.2023.362252.676037
Bita Salmanpoursohi; Amir Daneshvar; Shakiba Salmanpoursohi; Adel Pourghader Chobar; Fariba Salahi. "Cancer detection from textual data using a combination of machine learning approach". , 17, 3, 1403, 1001-1014. doi: 10.22059/ijms.2023.362252.676037
Salmanpoursohi, Bita, Daneshvar, Amir, Salmanpoursohi, Shakiba, Pourghader Chobar, Adel, Salahi, Fariba. (1403). 'Cancer detection from textual data using a combination of machine learning approach', , 17(3), pp. 1001-1014. doi: 10.22059/ijms.2023.362252.676037
Salmanpoursohi, Bita, Daneshvar, Amir, Salmanpoursohi, Shakiba, Pourghader Chobar, Adel, Salahi, Fariba. Cancer detection from textual data using a combination of machine learning approach. , 1403; 17(3): 1001-1014. doi: 10.22059/ijms.2023.362252.676037

Cancer detection from textual data using a combination of machine learning approach

Interdisciplinary Journal of Management Studies
دوره 17، شماره 3، پاییز 2024، صفحه 1001-1014    اصل مقاله (632.91 K)
نوع مقاله: SI: DBBD-2023
شناسه دیجیتال (DOI): 10.22059/ijms.2023.362252.676037
نویسندگان
Bita Salmanpoursohi1؛ Amir Daneshvar* 2؛ Shakiba Salmanpoursohi3؛ Adel Pourghader Chobar4؛ Fariba Salahi5
1Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
3Department of Information Technology Management, Tehran North Branch, Islamic Azad University, Tehran, Iran
4Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
5Department of Industrial Management, Tehran South Branch, Islamic Azad University, Tehran, Iran
چکیده
Recently, cancer has become one of the main diseases and causes of death of people all over the world. For this purpose, extensive research has been done on the prediction and early detection of this disease in the body of patients in different fields. Artificial intelligence and data mining approaches are among the methods that have helped researchers in diagnosing this disease. In this research, a machine learning approach for early and timely diagnosis of cancer disease is presented. For this purpose, it uses logistic regression techniques, Naive Bayes, two versions of Random Forest and Support Vector Machine, which work in parallel with each other. As a result of the integration of the techniques, the proposed system achieves higher accuracy and reduces errors compared to the basic methods. The performance of the proposed method was evaluated using different criteria and showed superior results compared to traditional methods.
کلیدواژه‌ها
Logistic Regression؛ Naive Bayes؛ Random Forest؛ Support Vector Machine؛ Cancer Detection
مراجع
آمار
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