An Advanced Multi-Input LSTM Framework with Attention for Predicting the Risk Level of Cardiovascular Disease

DOI

10.2991/978-94-6239-664-7_20How to use a DOI?

Keywords

Cardiovascular Disease Prediction; Long Short-Term Memory (LSTM); Multi-Input Deep Learning; Attention Mechanism; Uncertainty Quantification; Monte Carlo Dropout; Explainable AI; SHAP; LIME; Digital Twin Simulation; Clinical Decision Support; Risk Level Classification

Abstract

Cardiovascular disease (CVD) continues to be the leading cause of mortality globally. There is a need for accurate and clinically interpretable predictive systems for CVD. In this paper, we propose a multi-input Long Short-Term Memory (LSTM) model with an attention mechanism for predicting CVD, enhanced with uncertainty quantification via Monte Carlo Dropout and Bayesian-inspired techniques. To bridge predictive modeling with patient care, we further introduce a digital twin simulation for patient trajectory forecasting. The system integrates explainability tools, including attention heatmaps, SHAP, and LIME, alongside calibration analysis through reliability diagrams and Expected Calibration Error (ECE). Experimental results demonstrate strong predictive performance (AUC 0.77–0.81), reliable uncertainty estimates, and interpretable outputs, supporting its potential for clinical decision support.

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  - Arnob Aich Anurag
AU  - Jafir Islam Siam
AU  - Susanta Roy Emon
AU  - Nizhum Biswas Akash
AU  - Mohammad Saef Ullah Miah
PY  - 2026
DA  - 2026/06/08
TI  - An Advanced Multi-Input LSTM Framework with Attention for Predicting the Risk Level of Cardiovascular Disease
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 267
EP  - 284
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_20
DO  - 10.2991/978-94-6239-664-7_20
ID  - Anurag2026
ER  -