GENETIC ALGORITHM FOR FEATURE SELECTION IN NAÏVE BAYES IN LIFE RESISTANCE CLASSIFICATION ON BREAST CANCER PATIENT

Abstract

Breast cancer is the most common cancer in women's suffering and is the second 
leading cause of death for women (after lung cancer). More than one million cases and 
nearly 600,000 breast cancer deaths occur worldwide each year. Survival is generally 
defined as surviving patients over a period of time after the diagnosis of the disease. 
Accurate predictions about the likelihood of survival of breast cancer patients can allow 
doctors and healthcare providers to make more informed decisions about patient care. To 
classify the survival of breast cancer patients can do the utilization of data mining 
techniques with Naive Bayes algorithm. Naive Bayes is very simple and efficient but very 
sensitive to the features so from it the selection of the appropriate features is in need 
because irrelevant features can reduce the level of accuracy. Naive Bayes will work more 
effectively when combined with some attribute selection procedures such as Genetic 
Algorithm. In this study the researchers proposed the Genetic Algorithm for Feature 
Selection on Naive Bayes so as to improve the accuracy of breast cancer survival 
classification results. In this study using a private dataset breast cancer patients. The 
results show that Naive Bayes Genetic Algorithm has a higher accuracy of 90% compared 
to Naive Bayes with 86% accuracy