A hybrid recurrent neural network‐logistic chaos‐based whale optimization framework for heart disease prediction with electronic health records. (1st October 2020)

• Record Type: Journal Article
• Authors: Priyanga, P.; Pattankar, Veena V.; Sridevi, S.
• Is Part Of: Computational intelligence. Volume 37:Number 1(2021)
• Page Start: 315
• Page End: 343
• Publication Date: 2020-10-01
• Subjects: electronic health records -- heart disease prediction -- logistic chaos -- long short‐term memory -- recurrent neural network -- wolf optimization algorithm; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12405
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Heart disease, known interchangeably as "Cardio Vascular Disease, " blocks the blood vessels in the heart and causes heart attack, chest pain, and stroke. Heart disease is one of the leading causes of morbidity and mortality worldwide and it is one of the major causes of morbidity and mortality globally and a trending topic in clinical data analysis. Assessing risk factors related to heart disease is considered as an important step in diagnosing the disease at an early stage. Clinical data present in the form of electronic health records (EHR) can be extracted with the aid of machine learning (ML) algorithms to provide valuable decisions and predictions. ML approaches also play a vital role in early diagnosis and therapeutic monitoring of heart disease. Several research works have been carried out recently to predict heart disease. To this end, we propose a novel hybrid recurrent neural network (RNN)‐logistic chaos‐based whale optimization (LCBWO) structured hybrid framework for predicting heart disease within 5 years using EHR data. Meanwhile, in the hybrid model established multilayer bidirectional LSTM is used for feature selection, LCBWO algorithm for structural improvement and fast convergence, and LSTM for disease prediction. This research used 10 cross‐validations to obtain generalized accuracy and error values. The findings and observations provided here are focused on the knowledge obtained from the EHR report. The results show that the proposed novel hybrid RNN‐LCBWO framework achieves a higher accuracy of 98%, a specificity of 99%, precision of 96%, Mathews correlation coefficient of 91%, F‐measure of 0.9892, an area under the curve value of 98%, and a prediction time of 9.23 seconds. The accurate predictions obtained from the comparative analysis shows the significant performance of our proposed framework.