One out of four deaths is caused by heart related issues. Acting upon early signs of heart disease can, thus, drastically increase probability of saving lives. This paper discusses a cost-effective and reliable method of diagnosing heart abnormalities by using mobile phones that are nowadays typically available to an average user. A mobile application is developed to detect heart abnormal activities using either a digital stethoscope measurement as input, or a mobile recording of the heart beat using the mobile's microphone. To process the raw heart sound data, we first denoise the signal using wavelet transforms, and then apply machine learning techniques, namely, Convolutional Neural Networks for the classification of the stored heart sounds. A database consisting of recorded human heart sounds and their corresponding diagnosis is used to train the neural network. Moreover, neural network fine-tuning techniques such as ADAM Regularization is used to smoothen the prediction process. The proposed approach is tested on heart sound signals, that are 5 to 8 seconds long, and is shown to perform with an accuracy of 94.2% on the validation set.