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Zahmatkesh Zakariaee, Ali, Sadr, Hossein, Yamaghani, Mohamad Reza. (1402). A New Hybrid Method to Detect Risk of Gastric Cancer using Machine Learning Techniques. , 11(4), 505-515. doi: 10.22044/jadm.2023.13377.2464
Ali Zahmatkesh Zakariaee; Hossein Sadr; Mohamad Reza Yamaghani. "A New Hybrid Method to Detect Risk of Gastric Cancer using Machine Learning Techniques". , 11, 4, 1402, 505-515. doi: 10.22044/jadm.2023.13377.2464
Zahmatkesh Zakariaee, Ali, Sadr, Hossein, Yamaghani, Mohamad Reza. (1402). 'A New Hybrid Method to Detect Risk of Gastric Cancer using Machine Learning Techniques', , 11(4), pp. 505-515. doi: 10.22044/jadm.2023.13377.2464
Zahmatkesh Zakariaee, Ali, Sadr, Hossein, Yamaghani, Mohamad Reza. A New Hybrid Method to Detect Risk of Gastric Cancer using Machine Learning Techniques. , 1402; 11(4): 505-515. doi: 10.22044/jadm.2023.13377.2464

A New Hybrid Method to Detect Risk of Gastric Cancer using Machine Learning Techniques

Journal of AI and Data Mining
دوره 11، شماره 4، بهمن 2023، صفحه 505-515    اصل مقاله (780.78 K)
نوع مقاله: Original/Review Paper
شناسه دیجیتال (DOI): 10.22044/jadm.2023.13377.2464
نویسندگان
Ali Zahmatkesh Zakariaee1؛ Hossein Sadr* 2؛ Mohamad Reza Yamaghani3
1Department of Computer Engineering, Rahbord Shomal Institute of Higher Education, Rasht, Iran.
2Department of Health Informatics, Guilan Road Trauma Research Center, Trauma Institute, Guilan University of medical sciences, Rasht, Iran.
3Department of Computer Engineering, Faculty of Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran.
چکیده
Machine learning (ML) is a popular tool in healthcare while it can help to analyze large amounts of patient data, such as medical records, predict diseases, and identify early signs of cancer. Gastric cancer starts in the cells lining the stomach and is known as the 5th most common cancer worldwide. Therefore, predicting the survival of patients, checking their health status, and detecting their risk of gastric cancer in the early stages can be very beneficial. Surprisingly, with the help of machine learning methods, this can be possible without the need for any invasive methods which can be useful for both patients and physicians in making informed decisions. Accordingly, a new hybrid machine learning-based method for detecting the risk of gastric cancer is proposed in this paper. The proposed model is compared with traditional methods and based on the empirical results, not only the proposed method outperform existing methods with an accuracy of 98% but also gastric cancer can be one of the most important consequences of H. pylori infection. Additionally, it can be concluded that lifestyle and dietary factors can heighten the risk of gastric cancer, especially among individuals who frequently consume fried foods and suffer from chronic atrophic gastritis and stomach ulcers. This risk is further exacerbated in individuals with limited fruit and vegetable intake and high salt consumption.
کلیدواژه‌ها
Artificial Intelligence؛ Machine Learning؛ Gastric cancer؛ Hybrid Method؛ Neural Network
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