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.

中文翻译：

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具有电子健康记录的心脏病预测的混合递归神经网络-物流混沌混沌鲸鱼优化框架

心脏病（可互换地称为“心血管疾病”）会阻塞心脏中的血管，并引起心脏病，胸痛和中风。心脏病是全球发病率和死亡率的主要原因之一，也是全球发病率和死亡率的主要原因之一，也是临床数据分析中的一个热门话题。评估与心脏病有关的危险因素被认为是早期诊断该疾病的重要步骤。可以借助机器学习（ML）算法提取以电子健康记录（EHR）形式存在的临床数据，以提供有价值的决策和预测。ML方法在心脏病的早期诊断和治疗监测中也起着至关重要的作用。最近已经进行了一些研究工作来预测心脏病。为此，我们提出了一种新颖的混合递归神经网络（RNN）-基于逻辑混沌的鲸鱼优化（LCBWO）结构的混合框架，可使用EHR数据在5年内预测心脏病。同时，在混合模型中，建立的多层双向LSTM用于特征选择，LCBWO算法用于结构改进和快速收敛，LSTM用于疾病预测。这项研究使用10个交叉验证来获得广义的准确性和误差值。此处提供的发现和观察集中于从EHR报告中获得的知识。结果表明，提出的新型混合RNN-LCBWO框架可实现98％的更高准确度，99％的特异性，96％的精确度，在混合模型中，已建立的多层双向LSTM用于特征选择，LCBWO算法用于结构改进和快速收敛，LSTM用于疾病预测。这项研究使用10个交叉验证来获得广义的准确性和误差值。此处提供的发现和观察集中于从EHR报告获得的知识。结果表明，提出的新型混合RNN-LCBWO框架可实现98％的更高准确度，99％的特异性，96％的精确度，在混合模型中，已建立的多层双向LSTM用于特征选择，LCBWO算法用于结构改进和快速收敛，LSTM用于疾病预测。这项研究使用10个交叉验证来获得广义的准确性和误差值。此处提供的发现和观察集中于从EHR报告中获得的知识。结果表明，提出的新型混合RNN-LCBWO框架可实现98％的更高准确度，99％的特异性，96％的精确度，马修斯相关系数为91％，F度量为0.9892，曲线值下的面积为98％，预测时间为9.23秒。通过比较分析获得的准确预测表明了我们提出的框架的显着性能。