Breast Cancer Detection Using Ensemble Classifiers for Accuracy Improvement | ||
| Engineering Management and Soft Computing | ||
| مقاله 6، دوره 8، شماره 2 - شماره پیاپی 15، مهر 2022، صفحه 92-109 اصل مقاله (1.95 M) | ||
| نویسندگان | ||
| Mahboubeh Shamsi* 1؛ Mohadaseh Karimian2؛ Marziyeh Karimian3 | ||
| 1Assistant Prof. faculty of Electrical and Computer, Qom University of Technology, Qom, Irany. Email: shamsi@qut.ac.ir | ||
| 2Msc. of Computer Engineering, Faculty of Electrical and Computer Engineering, Shahab Danesh University, Qom, Iran. Email: m.karimian90@gmail.com | ||
| 3Msc. of Computer Engineering, Faculty of Electrical and Computer Engineering, Shahab Danesh University, Qom, Iran. Email: m.karimian64@gmail.com | ||
| چکیده | ||
| Early diagnosis of breast cancer plays a crucial role in treating the patient. Nowadays, data mining algorithms can provide intelligent methods in the health and treatment system that accurately detect breast cancer. The purpose of this study is breast cancer detection using ensemble classifier based on WBC and WDBC prepared databasesa. Our proposed model in the WBC database (reducing features by cfs+ optimizing samples using Resample+ ensemble classifier using data mining algorithms (kstar + random forest + Naïve Bayes and Bayes network)) has the best detection accuracy ( 100%), implementation time (0 seconds) and without any errors and on the WDBC database (reducing features by cfs+ optimizing samples using Resample+ ensemble classifier using data mining algorithms (IBK algorithm+ Naïve Bayes, Bayes network and kstar)) has an accuracy of 99/29, the implementation time is 0 seconds, and the mean absolute error is 0/007. The results of this study show that according to the ensemble classifier methods using data mining algorithms on the prepared database, new systems can be designed to help physicians that facilitate treatment processes. | ||
| کلیدواژهها | ||
| Accuracy Improvement؛ Data Mining؛ Ensemble Classifiers؛ Feature Selection؛ Sampling | ||
| عنوان مقاله [English] | ||
| -تشخیص سرطان سینه با استفاده از طبقهبندهای ترکیبی جهت بهبود دقت | ||
| نویسندگان [English] | ||
| محبوبه شمسی1؛ محدثه کریمیان2؛ مرضیه کریمیان3 | ||
| 1استادیار دانشکده برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران. رایانامه: shamsi@qut.ac.ir | ||
| 2کارشناسی ارشد مهندسی کامپیوتر، دانشکده مهندسی برق و کامپیوتر، دانشگاه شهاب دانش، قم، ایران. رایانامه: m.karimian90@gmail.com | ||
| 3کارشناسی ارشد مهندسی کامپیوتر، دانشکده مهندسی برق و کامپیوتر، دانشگاه شهاب دانش، قم، ایران. رایانامه: m.karimian64@gmail.com | ||
| چکیده [English] | ||
| تشخیص زودهنگام سرطان سینه نقش بسیار کلیدی در درمان بیمار ایفا میکند. امروزه الگوریتمهای دادهکاوی میتوانند روشهای هوشمندی در نظام سلامت ارائه دهند که با دقت بالایی سرطان سینه را تشخیص دهند. هدف از انجام این مطالعه، تشخیص سرطان سینه با استفاده از طبقهبندهای ترکیبی بر روی پایگاه دادهی آمادهسازی شدهی WBC و WDBC میباشد. مدل پیشنهادی ما در پایگاه دادهی WBC (کاهش ویژگیها با CFS+ بهینه کردن نمونه ها با روش Resample+ طبقه بند ترکیبی (kstar+ جنگل تصادفی+ شبکهی بیز و بیزین ساده))، دارای بهترین دقت تشخیص (% 100)، زمان پیادهسازی (0 ثانیه) و بدون هیچ خطایی میباشد و در پایگاه دادهی WDBC (کاهش ویژگیها با CFS+ بهینه کردن نمونه ها با روش Resample+ طبقه بند ترکیبی (الگوریتم IBK+ شبکهی بیز، بیزین ساده و kstar))، دارای دقت %99.29، زمان پیادهسازی 0 ثانیه و میانگین خطای مطلق 0.007 میباشد. نتایج این مطالعه نشان میدهد که با توجه به روشهای طبقهبند ترکیبی بر روی پایگاهدادهی آمادهسازی شده میتوان سیستمهای نوینی برای کمک به پزشکان طراحی نمود که موجب تسهیل در فرآیندهای تشخیصی و درمانی شوند. | ||
| کلیدواژهها [English] | ||
| انتخاب ویژگی, بهبود دقت, داده کاوی, طبقهبندهای ترکیبی, نمونه گیری | ||
| مراجع | ||
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