title: Predicting breast screening attendance using machine learning techniques.
abstract: Machine learning-based prediction has been effectively applied for many healthcare applications. Predicting breast screening attendance using machine learning (prior to the actual mammogram) is a new field. This paper presents new predictor attributes for such an algorithm. It describes a new hybrid algorithm that relies on back-propagation and radial basis function-based neural networks for prediction. The algorithm has been developed in an open source-based environment. The algorithm was tested on a 13-year dataset (1995-2008). This paper compares the algorithm and validates its accuracy and efficiency with different platforms. Nearly 80% accuracy and 88% positive predictive value and sensitivity were recorded for the algorithm. The results were encouraging; 40-50% of negative predictive value and specificity warrant further work. Preliminary results were promising and provided ample amount of reasons for testing the algorithm on a larger scale.
authors.affiliation: Ryerson University, TRSM, Toronto, Canada. vikraman@ryerson.ca
authors.forename: Vikraman
authors.initials: V
authors.lastname: Baskaran
authors.forename: Aziz
authors.initials: A
authors.lastname: Guergachi
authors.forename: Rajeev K
authors.initials: RK
authors.lastname: Bali
authors.forename: Raouf N G
authors.initials: RN
authors.lastname: Naguib
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D055088:Early Detection of Cancer / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008327:Mammography / Q000706:statistics & numerical data*; D008403:Mass Screening / Q000706:statistics & numerical data*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity