Heart disease prediction: Improved quantum convolutional neural network and enhanced features

Abstract

Currently, heart disease is the leading cause of death in world. Since cardiac sickness requires knowledge and detailed information, it is challenging to anticipate. Healthcare systems have been using Internet of Things (IoT) technologies for gathering sensor data for diagnosing the heart disease and prognosis in recent years. Researchers have focused a lot of emphasis on diagnosing heart disease, although the outcomes are not always reliable. This article proposes the automated heart disease prediction model with three main stages, including preprocessing, feature extraction, and prediction. The input data undergoes an improved Z-score normalization as the preprocessing step. The appropriate features needed to train the prediction model are retrieved from the preprocessed data during feature extraction. The features extracted include improved entropy, statistical features, and information gain features. Depends on the features extracted, prediction is determined by the Improved Quantum CNN (IQCNN). The results of the IQCNN are compared to earlier systems for a various metrics. The proposed IQCNN model has achieved better accuracy of 0.91 at a 70% learning rate when evaluated over conventional methods like Bi-LSTM, CNN, QCNN, DNN, NN, RNN, MDCNN and E-KNN for better performance in the prediction of heart disease.