ICCK Journal of Software Engineering
ISSN: 3069-1834 (Online)
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TY - JOUR AU - Farooq, Wajiha AU - Ali, Aamir AU - Fatima, Hafza Mehreen AU - Rafiq, Wasif AU - Zainab, Noor E AU - Ali, Misbah PY - 2026 DA - 2026/01/30 TI - FusedCNN-LSTM: A Software-Oriented Multimodal Deep Learning Framework for Intelligent Hypertension Risk Prediction JO - ICCK Journal of Software Engineering T2 - ICCK Journal of Software Engineering JF - ICCK Journal of Software Engineering VL - 2 IS - 1 SP - 11 EP - 29 DO - 10.62762/JSE.2025.995217 UR - https://www.icck.org/article/abs/JSE.2025.995217 KW - convolutional neural network KW - long short-term memory KW - hypertension KW - machine learning KW - deep learning AB - Hypertension, a life-threatening global health challenge, requires early detection to prevent severe cardiovascular complications. Fundus imaging reveals microvascular alterations, yet conventional diagnosis often misses subtle early changes. This study introduces a multimodal deep learning framework that integrates clinical data, fundus images, and demographic features to improve hypertension prediction. Unlike single-modality approaches, our method captures complementary risk factors from both structured and unstructured data. We evaluate machine learning and deep learning models on clinical data, confirming DL's superior accuracy. For fundus images alone, a CNN achieves 74.44% accuracy, highlighting the limitations of unimodal image analysis. To overcome this, we propose a fused CNN-LSTM architecture that models both spatial biomarkers and temporal clinical trends. The framework achieves robust performance, with an overall accuracy of 98% and minimal variation across datasets. Implemented in TensorFlow/Keras, the system adopts a modular, software-oriented design, ensuring flexibility, ease of maintenance, and seamless integration into clinical workflows. This holistic approach enables timely intervention, improves patient outcomes, and reduces healthcare burdens. SN - 3069-1834 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Farooq2026FusedCNNLS,
author = {Wajiha Farooq and Aamir Ali and Hafza Mehreen Fatima and Wasif Rafiq and Noor E Zainab and Misbah Ali},
title = {FusedCNN-LSTM: A Software-Oriented Multimodal Deep Learning Framework for Intelligent Hypertension Risk Prediction},
journal = {ICCK Journal of Software Engineering},
year = {2026},
volume = {2},
number = {1},
pages = {11-29},
doi = {10.62762/JSE.2025.995217},
url = {https://www.icck.org/article/abs/JSE.2025.995217},
abstract = {Hypertension, a life-threatening global health challenge, requires early detection to prevent severe cardiovascular complications. Fundus imaging reveals microvascular alterations, yet conventional diagnosis often misses subtle early changes. This study introduces a multimodal deep learning framework that integrates clinical data, fundus images, and demographic features to improve hypertension prediction. Unlike single-modality approaches, our method captures complementary risk factors from both structured and unstructured data. We evaluate machine learning and deep learning models on clinical data, confirming DL's superior accuracy. For fundus images alone, a CNN achieves 74.44\% accuracy, highlighting the limitations of unimodal image analysis. To overcome this, we propose a fused CNN-LSTM architecture that models both spatial biomarkers and temporal clinical trends. The framework achieves robust performance, with an overall accuracy of 98\% and minimal variation across datasets. Implemented in TensorFlow/Keras, the system adopts a modular, software-oriented design, ensuring flexibility, ease of maintenance, and seamless integration into clinical workflows. This holistic approach enables timely intervention, improves patient outcomes, and reduces healthcare burdens.},
keywords = {convolutional neural network, long short-term memory, hypertension, machine learning, deep learning},
issn = {3069-1834},
publisher = {Institute of Central Computation and Knowledge}
}
Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits 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 licence, and indicate if changes were made. 
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