BACKGROUND Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. OBJECTIVE To investigate whether there are differences in performance of machine learning models trained and evaluated acros...

Objective: Breast cancer is one of the most common cancers affecting women. Both physicians and patients have concerned about breast cancer survivability. Many researchers have studied the breast cancer survivability applying artificial nerural network model (ANN). Usually ANN model outperformed in classification of breast cancer survivability than other models such as logistic regression, Baye...

Prediction of cancer survivability using machine learning techniques has become a popular approach in recent years. ‎In this regard, an important issue is that preparation of some features may need conducting difficult and costly experiments while these features have less significant impacts on the final decision and can be ignored from the feature set‎. ‎Therefore‎, ‎developing a machine for p...

Each year number of deaths is increasing extremely because breast cancer. It the most frequent type all cancers and major cause death in women worldwide. Any development for prediction diagnosis cancer disease capital important a healthy life. Consequently, high accuracy to update treatment aspect survivability standard patients. Machine learning techniques can bring large contribute on process...

Building the survivability prediction models is a challenging task because they provide an important approach to assessing risk and prognosis. In this paper, we investigated the performance of combining of the Bagging with several weak learners to build 5-accurate breast cancer survivability prediction models from the Srinagarind hospital database in Thailand. These models could assist medical ...

Breast cancer is the most frequently diagnosed cancer in women. Using historical patient information stored in clinical datasets, data mining and machine learning approaches can be applied to predict the survival of breast cancer patients. A common drawback is the absence of information, i.e., missing data, in certain clinical trials. However, most standard prediction methods are not able to ha...

Breast cancer is one of leading causes of death. This study predicts 5-year survivability of breast cancer patients by two data mining techniques. The data set consisted of information about patients who have cancer diagnosis collected by SEER. In this study, data set is pre-classified into survival and non-survival with 90.66% and 9.34%, respectively. The selected variables used to predict 5-y...