EP-1210: Naïve bayes models for predicting the risk of loco-regional relapse in breast cancer patients

2014 ◽  
Vol 111 ◽  
pp. S59
Author(s):  
S. Tortajada ◽  
J.L. Lopez Guerra ◽  
D. Palacios ◽  
A. Pérez-González ◽  
J.M. García-Gómez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Breast Cancer Patients

scholarly journals Hasil Analisis Teknik Data Mining dengan Metode Naive Bayes untuk Mendiagnosa Penyakit Kanker Payudara

2020 ◽  
Vol 1 (2) ◽  
pp. 130
Author(s):  
Elma Tiana ◽  
Sri Wahyuni
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Feature Selection ◽  
Cell Growth ◽  
Cancer Patients ◽  
Missing Values ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Breast Cancer Patients ◽  
Last Stage

Breast cancer or Mammae Carsinoma is an uncontrolled cell growth in the milk-producing glands (lobular), the gland tract from the lobular to the Breast nipple (ductus), and the breast support tissues that surround the lobular, ductus, vessels Blood and limfe vessels, but does not include breast skin. Research begins by conducting a preprocessing stage, to eliminate missing values. After that the process is imputasi to remove missing values. It then performed a feature selection to see which attribute had a major impact on the data. The last stage is classification with two methods, namely Naïve Bayes. At the end of the study, the method is best to classify the recurrence data of breast cancer patients.

KERNEL-BASED NAIVE BAYES CLASSIFIER FOR BREAST CANCER PREDICTION

2007 ◽  
Vol 15 (01) ◽  
pp. 17-25 ◽  
Author(s):  
JESMIN NAHAR ◽  
YI-PING PHOEBE CHEN ◽  
SHAWKAT ALI
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Breast Cancer Patients ◽  
Cancer Prediction ◽  
Early Cancer Diagnosis ◽  
Cancer Tumor ◽  
Bayes Algorithm

The classification of breast cancer patients is of great importance in cancer diagnosis. Most classical cancer classification methods are clinical-based and have limited diagnostic ability. The recent advances in machine learning technique has made a great impact in cancer diagnosis. In this research, we develop a new algorithm: Kernel-Based Naive Bayes (KBNB) to classify breast cancer tumor based on memography data. The performance of the proposed algorithm is compared with that of classical navie bayes algorithm and kernel-based decision tree algorithm C4.5. The proposed algorithm is found to outperform in the both cases. We recommend the proposed algorithm could be used as a tool to classify the breast patient for early cancer diagnosis.

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