Araghi Niknam, Faeze, Ghousi, Rouzbeh, Masoumi, AmirHossein, Atashi, Alireza, Makui, Ahmad. (1400). Hybrid Medical Data Mining Model for Identifying Tumor Severity in Breast Cancer Diagnosis. , 55(2), 151-164. doi: 10.22059/jieng.2021.326775.1789
Faeze Araghi Niknam; Rouzbeh Ghousi; AmirHossein Masoumi; Alireza Atashi; Ahmad Makui. "Hybrid Medical Data Mining Model for Identifying Tumor Severity in Breast Cancer Diagnosis". , 55, 2, 1400, 151-164. doi: 10.22059/jieng.2021.326775.1789
Araghi Niknam, Faeze, Ghousi, Rouzbeh, Masoumi, AmirHossein, Atashi, Alireza, Makui, Ahmad. (1400). 'Hybrid Medical Data Mining Model for Identifying Tumor Severity in Breast Cancer Diagnosis', , 55(2), pp. 151-164. doi: 10.22059/jieng.2021.326775.1789
Araghi Niknam, Faeze, Ghousi, Rouzbeh, Masoumi, AmirHossein, Atashi, Alireza, Makui, Ahmad. Hybrid Medical Data Mining Model for Identifying Tumor Severity in Breast Cancer Diagnosis. , 1400; 55(2): 151-164. doi: 10.22059/jieng.2021.326775.1789

Hybrid Medical Data Mining Model for Identifying Tumor Severity in Breast Cancer Diagnosis

Advances in Industrial Engineering
دوره 55، شماره 2، تابستان 2021، صفحه 151-164    اصل مقاله (1.08 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jieng.2021.326775.1789
نویسندگان
Faeze Araghi Niknam1؛ Rouzbeh Ghousi* 1؛ AmirHossein Masoumi1؛ Alireza Atashi2؛ Ahmad Makui1
1School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.
2Department of Medical Informatics, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
چکیده
Purpose: This study proposes a methodology for detecting tumor severity using data mining of databases relating to breast imaging modalities. In doing so, it proposes creating a software application that can serve as an efficient decision-making support system for medical practitioners, especially those in areas where there is a shortage of modern medical diagnostic devices or specialized practitioners, such as in developing countries.
Method: we investigated the data of approximately 3754 screened women by using “BI-RADS” categories as a quality assessment tool to screening, measure, and identify the size and location of lesions, determine the number of lymph nodes, collect biopsy samples, determine final diagnoses, prognoses, and age which were all available from the screening registry.
Result: The application of each algorithm on BI-RADS values 4 and 5 for Invasive Ductal Carcinoma lesions was assessed, and the following accuracy was acquired: CART: 84.71%. In order to get the best result, four optimum clusters based on tumor size were applied to constructing simple rules with significant confidence.
Conclusion: This study presents a hybrid approach - a combination of k-means with GRI and CART decision tree - to better assess breast cancer data sets.
کلیدواژه‌ها
Breast Cancer Prediction؛ Mammography؛ Ultrasonography؛ Medical Data Mining؛ Invasive Ductal Carcinoma
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