Prediction of Breast Cancer Survival Through Knowledge Discovery in Databases

Abstract

The collection of large volumes of medical data has offered an opportunity to develop prediction models for survival by the medical research community. Medical researchers who seek to discover and extract hidden patterns and relationships among large number of variables use knowledge discovery in databases (KDD) to predict the outcome of a disease. The study was conducted to develop predictive models and discover relationships between certain predictor variables and survival in the context of breast cancer. This study is Cross sectional. After data preparation, data of 22,763 female patients, mean age 59.4 years, stored in the Surveillance Epidemiology and End Results (SEER) breast cancer dataset were analyzed anonymously. IBM SPSS Statistics 16, Access 2003 and Excel 2003 were used in the data preparation and IBM SPSS Modeler 14.2 was used in the model design. Support Vector Machine (SVM) model outperformed other models in the prediction of breast cancer survival. Analysis showed SVM model detected ten important predictor variables contributing mostly to prediction of breast cancer survival. Among important variables, behavior of tumor as the most important variable and stage of malignancy as the least important variable were identified. In current study, applying of the knowledge discovery method in the breast cancer dataset predicted the survival condition of breast cancer patients with high confidence and identified the most important variables participating in breast cancer survival.

Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database

“…Using only a very limited set of clinical variables, we demonstrate that our models are capable of predicting meaningful clinical outcomes. Previous studies using the SEER database have used various machine learning methods for diagnosis and prognosis purposes in breast [11][12][13][14][15] and lung cancers, 16,17 but have not applied these techniques to the SEER data on meningiomas. As compared to classical statistical approaches, the value of predictive modeling is the ability to obtain predictions for individual patients rather than group means.…”

Machine learning is a valid method for predicting prehospital delay after acute ischemic stroke

“…SVM is an ML algorithm with a good regularization attribute that is based on the structural risk minimization principle of statistical learning (Xiang et al., 2019). The SVM optimization process maximizes prediction accuracy and reduces over‐fitting of training data (Lotfnezhad, Ahmadi, Roudbari, & Sadoughi, 2015).…”

Predicting breast cancer 5-year survival using machine learning: A systematic review

“…Among the 31 studies, 9 had a high risk of bias [2,25,27,28,43,44,46,48,50], 17 had a moderate risk of bias [24, 26, 29-35, 39, 41, 42, 45, 47, 49, 51, 52], and 5 ad a low risk of bias [23,[36][37][38]40], as shown in Table 1.…”

“…For the class imbalance, 24 studies showed class imbalance in the samples of the final model construction [2, 23, 24, 26, 28-37, 40-45, 47-50], and 7 of them dealt with this problem [28,29,34,37,42,47,49]. The methods included undersampling, bagging algorithm, SMOTE, PSO, K-means, KNN, and bagging.…”