Prediction of Congestive Heart Failure (Chf) Ecg Data Using Machine Learning

Abstract

Machine learning can learn a complex system using a large amount of data and has the potential in predicting critical medical emergencies like congestive heart failure (CHF). In this paper, a machine learning algorithm is proposed for predicting CHF using convolutional neural network (CNN). The system uses a VGG16 model, which is dependent on ImageNet and utilizes ECG signal. The CHF information is obtained from the Beth Israel Deaconess Medical Centre (BIDMC) CHF dataset and also the typical dataset from the FANTASIA database. Various models of CNN were prepared and tested on the dataset to obtain the information about CHF or normal for a patient until the required accuracy is achieved. The performance is assessed based on the accuracy of the code and precision. The ECG signal of a patient recorded for 24 hours is utilized in this research. The experimental result obtained by this research has given accuracy of 100% on applying the VGG16 Model to the dataset. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.