{"title":"基于混合深度学习分类模型的有效心脏病预测","authors":"Vaishali Baviskar , Madhushi Verma , Pradeep Chatterjee , Gaurav Singal","doi":"10.1016/j.irbm.2023.100786","DOIUrl":null,"url":null,"abstract":"<div><p><strong>INTRODUCTION:</strong><span> 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.</span></p><p><strong>OBJECTIVES:</strong><span><span> 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, </span>particle swarm optimization and African buffalo algorithm have been implemented.</span></p><p><strong>METHODS:</strong><span> 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.</span></p><p><strong>RESULTS:</strong> 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.</p><p><strong>CONCLUSION:</strong> The obtained results show that the proposed system is comparatively more efficient for heart disease diagnosis than other conventional techniques.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient Heart Disease Prediction Using Hybrid Deep Learning Classification Models\",\"authors\":\"Vaishali Baviskar , Madhushi Verma , Pradeep Chatterjee , Gaurav Singal\",\"doi\":\"10.1016/j.irbm.2023.100786\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><strong>INTRODUCTION:</strong><span> 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.</span></p><p><strong>OBJECTIVES:</strong><span><span> 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, </span>particle swarm optimization and African buffalo algorithm have been implemented.</span></p><p><strong>METHODS:</strong><span> 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.</span></p><p><strong>RESULTS:</strong> 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.</p><p><strong>CONCLUSION:</strong> The obtained results show that the proposed system is comparatively more efficient for heart disease diagnosis than other conventional techniques.</p></div>\",\"PeriodicalId\":14605,\"journal\":{\"name\":\"Irbm\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Irbm\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1959031823000350\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Irbm","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1959031823000350","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
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.
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-
…