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  - Fused-CNN-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 = {Fused-CNN-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}
}