基于混合深度学习分类模型的有效心脏病预测

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2023-10-01 DOI:10.1016/j.irbm.2023.100786
Vaishali Baviskar , Madhushi Verma , Pradeep Chatterjee , Gaurav Singal
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引用次数: 0

摘要

引言:心脏病(HD)已被确定为一种致命疾病,影响着全世界所有年龄段的人类。在这种情况下,数据挖掘(DM)技术已被发现在处理更大的患者数据集的同时,在HD并发症阶段的分析和预测中是有效的。该数据集也将由不相关和冗余的特征组成。这些特征将进一步影响分类过程中数据处理的准确性和速度。目标:因此,需要特征选择技术来从数据集中去除冗余特征。因此,在本研究中,已经实现了遗传算法、粒子群优化和非洲水牛算法等特征选择技术。方法:为了进一步增强这一过程,提出了一种新开发的GSA(遗传正弦算法),因为它能够选择最优特征,避免陷入局部最优。通过RNN(递归神经网络)结合LSTM(长短期记忆)算法对所选特征进行分类。为了过滤掉所有无效信息,只强调关键信息,开发了DPA-RNN+LSTM(Deep Progressive Attention RNN+LSTM)来提高分类率。结果:在两个基准数据集,即心脏病诊断UCI数据集和心力衰竭临床数据集上进行的性能和比较分析支持了所提出的结果。此外,还对Mann-Whitney U检验、Pearson相关系数、Friedman秩和Iman-Davenport显著值的统计分析进行了评估。结论:与其他常规技术相比,所提出的系统在心脏病诊断方面相对更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Efficient Heart Disease Prediction Using Hybrid Deep Learning Classification Models

Efficient Heart Disease Prediction Using Hybrid Deep Learning Classification Models

INTRODUCTION: Heart disease (HD) has been identified as one of the deadly diseases, which affects the human beings of all ages worldwide. In such a scenario, Data Mining (DM) techniques have been found to be efficient in the analysis and the prediction of the phases of HD complications while handling larger patient datasets'. This dataset would consist of irrelevant and redundant features as well. These features would further impact the accuracy and the speed of data processing during the classification process.

OBJECTIVES: Hence, the feature selection techniques are required for removing the redundant features from the dataset. Therefore, in this study, feature selection techniques like genetic algorithm, particle swarm optimization and African buffalo algorithm have been implemented.

METHODS: To further enhance this process, a newly developed GSA (Genetic Sine Algorithm) is proposed as it is capable of selecting optimal features and avoid getting trapped in local optima. The selected features are subjected to the classification technique by RNN (Recurrent Neural Network) integrated with LSTM (Long Short Term Memory) algorithm. To filter out all the invalid informations and emphasize only on critical information, DPA-RNN+LSTM (Deep Progressive Attention-RNN+LSTM) has been developed so as to improve the classification rate.

RESULTS: The proposed results have been supported by the performance and comparative analysis performed on two benchmark datasets namely heart disease diagnosis UCI dataset and heart failure clinical dataset. Further, statistical analysis in terms of Mann-Whitney U-test, Pearson Correlation co-efficient, Friedman rank and Iman-Davenport significant values has been evaluated.

CONCLUSION: The obtained results show that the proposed system is comparatively more efficient for heart disease diagnosis than other conventional techniques.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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