مورد إلكتروني
Classification of Breast Cancer Using Data Mining
| العنوان: | Classification of Breast Cancer Using Data Mining |
|---|---|
| المصدر: | American Scientific Research Journal for Engineering, Technology, and Sciences; Vol. 51 No. 1 (2019); 38-46; 2313-4402; 2313-4410 |
| بيانات النشر: | Mohammad Nassar for Researches (MNFR) 2019-01-04 |
| تفاصيل مُضافة: | Sardouk, Farah Deniz Duru, Dr. Adil Bayat, Dr. Oğuz |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | According to the publications of leading health organization in the world, the World Health Organization (WHO) reveals that breast cancer is the most propagated disease among women and it may end with mortality. The precautions and regular investigations are the options for preventing this cancer. Furthermore, the recognition of the sickness may begin at early stages for combating purpose. From data science perspectives, data mining technology is used to uncover the disease according to some parameters like BMI, age and sugar routine database. The deployment of those technologies had resulted in considerable results that may help for breast cancer aid. In this research, Coimbra dataset are collected and studied according to 10 predictors. We used these predictors to estimate if the breast cancer is occurring or not. The 6 algorithms used are compared according to their performance in WEKA and in MATLAB. The comparison is useful to prove the possibility of using Data Mining algorithms to help Medicine decision engine with good precision. |
| مصطلحات الفهرس: | ANN, Artificial Neural Network, BMI, KDD, k-fold cross validation, PPV, WHO., info:eu-repo/semantics/article, info:eu-repo/semantics/publishedVersion, Peer-reviewed Article |
| URL: | |
| الإتاحة: | Open access content. Open access content Copyright (c) 2019 American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS) |
| ملاحظة: | application/pdf English |
| أرقام أخرى: | JOASR oai:ojs.asrjetsjournal.org:article/4585 1090940299 |
| المصدر المساهم: | AMERICAN SCIENT RES JOURNAL (ASRJETS) From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.on1090940299 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |