Efficient Classification of Cardiovascular Abnormalities by Deep Learning

Medical technology in the world is growing rapidly over time. Even though technology develops innovative and efficient methodologies, but still people living in rural areas unable to use those facilities. Advancements in machine learning techniques help doctors to take decision on patient data easily. The effort of doctors to classify abnormal or normal subjects has been reduced due to technological advancements in Computer Aided Diagnosis (CAD). These advancements not only minimize doctors’ effort but it also reduces error rate in the misclassification. This research proposes a model that predicts cardiovascular abnormalities using machine learning and deep learning. K nearest neighbors (KNN), Convolution neural networks (CNN) are used to find effective disease identification. CNN provides highest accuracy when compared with KNN.

Hali tarjima qilinmagan