This paper presents a predictive space aggregated regression based boosting algorithm, and its application in classifying the Continuous Wave(CW) Flow Doppler image data set with the diseases of stenosis and regurgitation in mitral and aortic valves. The proposed algorithm involves finding a way to simultaneously combine all the weak learners based on a well-justified assumption as in the previous work that not only the weak learners but each training sample should have different contributions toward learning the final strong hypothesis. However, the proposed algorithm greatly improves on the previous method by (1) dramatically reducing the number of combination weights, leading to a more stable numerical solution, (2) having regularization in both data and predictive spaces to reduce the generalization error of the model, and (3) using the sparse weight selection scheme in the testing to further avoid overfitting. A sparse subset of the training data is chosen to best approximate the test sample, and the final hypothesis is constructed based only on the chosen training samples and associated weak learner weights. Finally, we empirically show that the proposed technique not only successfully solves the overfitting problem but also significantly increases the performance of the weak classifiers via a set of comparison experiments on the CW Flow Doppler image data set consisting of 4 types of valvular diseases at different severity levels. © 2013 IEEE.