Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis

Jonathan L. Jesneck, Loren W. Nolte, Jay A. Baker, Carey E. Floyd, Joseph Y. Lo
2006 Medical Physics (Lancaster)  
As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. This dissertation presents a computer aid known as optimized decision fusion, and explores
more » ... both its underlying theory and practical application. The purpose of this work was 1) to present optimized decision fusion, a classification algorithm designed for noisy, heterogeneous data sets with few samples, and 2) to evaluate decision fusion's classification ability on clinical, heterogeneous breast cancer data sets. This study used the following three clinical data sets: heterogeneous breast mass lesions, heterogeneous breast microcalcification lesions, and breast blood serum protein levels. In addition to these clinical data sets, we also used various simulated data sets. We used two variants of our decision fusion algorithm: 1) DF-A, which optimized the area (AUC) under the receiver operating characteristic (ROC) curve, an DF-P, which optimized the high-sensitivity partial area (pAUC) under the curve. We compared decision fusion's classification performance to those of the following other classifiers: linear discriminant analysis, an artificial neural network, classical regression models (linear, logistic, and probit), Bayesian model averaging of these regression models, least angle regression, and a support vector machine.
doi:10.1118/1.2208934 pmid:16964873 pmcid:PMC2569003 fatcat:av3yxepgordtjhdwn42ssv2ti4