AI Article Synopsis
- The paper discusses a novel machine learning approach for predicting breast screening attendance before mammograms, introducing new predictor attributes and a hybrid algorithm combining back-propagation and radial basis function-based neural networks.
- The algorithm was developed in an open-source environment and tested on a comprehensive 13-year dataset, achieving approximately 80% accuracy and notable positive predictive value and sensitivity.
- While the results were promising, particularly the high accuracy rates, the authors noted that negative predictive value and specificity need improvement, suggesting the need for further research with larger datasets.
Article 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.
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| http://dx.doi.org/10.1109/TITB.2010.2103954 | DOI Listing |
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