title: Machine learning models in breast cancer survival prediction.
abstract: BACKGROUND
  Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets.
  
  
  OBJECTIVE
  The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival.
  
  
  METHODS
  We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve.
  
  
  RESULTS
  Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%).
  
  
  CONCLUSIONS
  This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for breast cancer survival prediction as well as medical decision making.
authors.affiliation: Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.|Computer Engineering Department, Shahid Bahonar University, Kerman, Iran.
authors.forename: Mitra
authors.initials: M
authors.lastname: Montazeri
authors.affiliation: Social Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.|Department of Computer, Technical and Vocational University, Kerman, Iran.
authors.forename: Mohadeseh
authors.initials: M
authors.lastname: Montazeri
authors.affiliation: Research Center for Modeling in Health, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
authors.forename: Mahdieh
authors.initials: M
authors.lastname: Montazeri
authors.affiliation: Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
authors.forename: Amin
authors.initials: A
authors.lastname: Beigzadeh
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000145:classification* / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D055088:Early Detection of Cancer / Q000379:methods*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015996:Survival Rate; D055815:Young Adult
keywords: Breast cancer survival prediction; classification; machine learning models