PROGNOSIS OF BREAST CANCER BASED ON A FUZZY CLASSIFICATION METHOD

2010 ◽  
Keyword(s):  
Breast Cancer ◽  
Fuzzy Classification ◽  
Classification Method

Perbandingan CART dan Random Forest untuk Deteksi Kanker berbasis Klasifikasi Data Microarray

2020 ◽  
Vol 4 (5) ◽  
pp. 805-812
Author(s):  
Riska Chairunisa ◽  
Adiwijaya ◽  
Widi Astuti
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Ovarian Cancer ◽  
Random Forest ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Discrete Wavelet ◽  
Prostate Tumors ◽  
Cancer Data ◽  
Colon Tumors

Cancer is one of the deadliest diseases in the world with a mortality rate of 57,3% in 2018 in Asia. Therefore, early diagnosis is needed to avoid an increase in mortality caused by cancer. As machine learning develops, cancer gene data can be processed using microarrays for early detection of cancer outbreaks. But the problem that microarray has is the number of attributes that are so numerous that it is necessary to do dimensional reduction. To overcome these problems, this study used dimensions reduction Discrete Wavelet Transform (DWT) with Classification and Regression Tree (CART) and Random Forest (RF) as classification method. The purpose of using these two classification methods is to find out which classification method produces the best performance when combined with the DWT dimension reduction. This research use five microarray data, namely Colon Tumors, Breast Cancer, Lung Cancer, Prostate Tumors and Ovarian Cancer from Kent-Ridge Biomedical Dataset. The best accuracy obtained in this study for breast cancer data were 76,92% with CART-DWT, Colon Tumors 90,1% with RF-DWT, lung cancer 100% with RF-DWT, prostate tumors 95,49% with RF-DWT, and ovarian cancer 100% with RF-DWT. From these results it can be concluded that RF-DWT is better than CART-DWT.  

scholarly journals A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Jinyu Cong ◽  
Benzheng Wei ◽  
Yunlong He ◽  
Yilong Yin ◽  
Yuanjie Zheng
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Ensemble Classification ◽  
Classification Method ◽  
Ultrasound Images ◽  
Selective Ensemble ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Breast cancer has been one of the main diseases that threatens women’s life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning.

scholarly journals A quantifier-based fuzzy classification system for breast cancer patients

2013 ◽  
Vol 58 (3) ◽  
pp. 175-184 ◽  
Author(s):  
Daniele Soria ◽  
Jonathan M. Garibaldi ◽  
Andrew R. Green ◽  
Desmond G. Powe ◽  
Christopher C. Nolan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Classification System ◽  
Fuzzy Classification ◽  
Breast Cancer Patients ◽  
Fuzzy Classification System
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