As part of George B. Moody Physionet Challenge 2022, our team Melbourne Kangas, proposed an algorithm for identifying abnormal heart sounds from paediatric phonocardiograms (PCGs). We developed a Deep Learning (DL) approach and a handcrafted feature-based approach. The DL classifier was based on bidirectional long-short-termmemory and Mel-frequency cepstrum coefficients from raw PCG signals. The feature-based approach used nonnegative matrix factorisation to denoise PCG signals and then extracted the features based on the whole and segmented recordings, followed by feature selection. A random under-sampling boosting classifier for murmur classification and robust boosting classifier for outcome classification were given the subset of features. The feature-based performed better than the DL classifiers on the validation set. The feature-based classifier received a weighted accuracy of 0.632 (29th out of 41 teams) and a challenge cost of 11,735 (3rd out of 39 teams) on the test set. Decision fusion of the two approaches decreased 10-fold crossvalidation results.