Early Detection of Myocardial Infarction Using LSTM-Based Multimodel Intelligence Framework

DOI

10.2991/978-94-6239-693-7_5How to use a DOI?

Keywords

Myocardial Infarction; LSTM Network; Deep Learning; Early Diagnosis; Preventive Healthcare

Abstract

One of the leading causes of death worldwide is myocardial infarction (MI), for which prompt diagnosis is essential to averting serious cardiac events. Long Short Term Memory (LSTM) networks trained on sequential clinical data are used in this work [1], [2] to present a multimodal intelligent framework for early MI detection. To find minute changes in a patient’s health, the system examines time-dependent metrics like blood pressure, heart rate, cholesterol, and glucose levels. The model successfully uncovers hidden patterns in clinical records [3] by utilizing LSTM’s capacity to learn long-term temporal dependencies. Multiple physiological signals are integrated by a fusion layer to improve diagnostic precision and dependability. Additionally, the framework uses a cloud-based platform for automated alert generation, continuous monitoring, and real-time data processing. This method provides a scalable and economical solution for intelligent cardiac care by facilitating early risk prediction and prompt intervention. All things considered, the model shows how deep learning can be used to improve cardiovascular healthcare prevention.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - B. Sandhiya
AU  - R. Sangavi
AU  - K. Santhiya
PY  - 2026
DA  - 2026/06/16
TI  - Early Detection of Myocardial Infarction Using LSTM-Based Multimodel Intelligence Framework
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 33
EP  - 41
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_5
DO  - 10.2991/978-94-6239-693-7_5
ID  - Sandhiya2026
ER  -