Document Type : SI: DBBD-2023
Authors
1
Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2
Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
3
Department of Information Technology Management, Tehran North Branch, Islamic Azad University, Tehran, Iran
4
Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
5
Department of Industrial Management, Tehran South Branch, Islamic Azad University, Tehran, Iran
10.22059/ijms.2023.362252.676037
Abstract
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.
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