title: 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.
authors.affiliation: 1. Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran 2. Department of Health Information Technology (HIT), Faculty of Paramedicine, Urmia University of Medical Sciences, Urmia, Iran. hadi.afshar@gmail.com.
authors.forename: Hadi
authors.initials: H
authors.lastname: Lotfnezhad Afshar
authors.forename: Maryam
authors.initials: M
authors.lastname: Ahmadi
authors.forename: Masoud
authors.initials: M
authors.lastname: Roudbari
authors.forename: Farahnaz
authors.initials: F
authors.lastname: Sadoughi
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000401:mortality*; D003430:Cross-Sectional Studies; D057225:Data Mining / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008875:Middle Aged; D018426:SEER Program / Q000706:statistics & numerical data*; D060388:Support Vector Machine; D016019:Survival Analysis; D014481:United States / Q000453:epidemiology; D055815:Young Adult