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Age Specific Analysis on Multiclass Sequential Curated-Electronic Health Records (MSC-EHR) for CAD Survival Prediction using Deep Learning Techniques

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Abstract

Early risk assessment is essential for addressing cardiovascular disease, a major healthcare issue. Accurate diagnosis is essential for prompt medical care and medication. Deep learning (DL) approaches have yielded promising outcomes in the detection of coronary artery disease (CAD). Previous work [Waqar et al. Sci Programm. 2021;2021:1–12, Muntasir Nishat et al. Sci Programm. 2022;2022:1–17, Krishnan et al. Int J Electr Comput Eng. 2021;11(6):2088–8708] is mostly based on open repository data, it is necessary to use real-world data to observe the DL-based models’ performance. For this cutting-edge effort, real-world EHR is collected from Excelcare Hospital in Guwahati, Assam, India with three timelines spaced at six-month intervals concatenated into multiclass sequential curated-electronic health records (MSC-EHR). The FRS risk estimating method is used to convert multiclass categorization with four risk labels on the curated dataset. Hence, this work aims at harnessing the benefits of MSC-EHR data with age-specific cluster (ASC) over age-agnostic cluster (AAC) for improved CAD prediction. This work proposes two hybrid models based on deep learning methodology. The study has been divided into two phases to analyze AAC and ASC datasets independently. The purpose of this research endeavor is to analyze the performance of hybrid models using deep learning techniques on curated dataset and investigate the impact of data pre-processing and balancing techniques on the model performance. For the phase 1 experimentation, Hybrid-Model1 (RNN+GRU) achieved 93.27%, and Hybrid-Model2 (LSTM+GRU) achieved 94.01% accuracy, while in phase 2, Hybrid-Model1 attained 96.93% (ASC2) accuracy, and Hybrid-Model2 attained 97.28% (ASC2) accuracy, highlighting the importance of ASC data over AAC in CAD prediction.

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Data Availability and Access

The data that support the findings of this study are available from the Excelcare Hospital, Guwahati, India, but restrictions apply to the availability of these data, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of the Managing Director and HOD of Cardiology Excelcare Hospital, Guwahati, India.

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Acknowledgements

The authors would like to thank the Managing Director and HOD of Cardiology Excelcare Hospital, Guwahati, India, for help in collecting heart disease data as well as the many members of the department, including laboratory personnel and management.

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Authors

Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection and analysis were performed by Ms. Smita. The first draft of the manuscript was written by Ms. Smita, and other authors commented on previous versions of the manuscript.

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Correspondence to Smita.

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Our paper has no potential Conflict of interest. All authors have read and approved the work for submission to your journal.

Ethical and Informed Consent for Data Used

Research-specific study ethical approval for the use of clinical samples and retrieving clinical data was approved by the Managing Director and HOD of Cardiology Excelcare Hospital, Guwahati, India. Clinical data collected during the COVID period and approval received through Dr. NEIL BARDOLOI (MBBS, MD, DM (AIIMS), FACC, FESC, Managing Director and HOD, Cardiology Excelcare Hospital, Guwahati, India) with electronic mail: drbardoloineil@gmail.com.

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This article is part of the topical collection “Security for Communication and Computing Application” guest edited by Karan Singh, Ali Ahmadian, Ahmed Mohamed Aziz Ismail, R S Yadav, Md. Akbar Hossain, D. K. Lobiyal, Mohamed Abdel-Basset, Soheil Salahshour, Anura P. Jayasumana, Satya P. Singh, Walid Osamy, Mehdi Salimi and Norazak Senu.

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Smita, Kumar, E. Age Specific Analysis on Multiclass Sequential Curated-Electronic Health Records (MSC-EHR) for CAD Survival Prediction using Deep Learning Techniques. SN COMPUT. SCI. 5, 603 (2024). https://doi.org/10.1007/s42979-024-02946-7

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