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Paper details
Number 4 - December 2024
Volume 34 - 2024
A deep learning based hybrid model for maternal health risk detection and multifaceted emotion analysis in social networks
R. Geethanjali, A. Valarmathi
Abstract
In the field of public health, accurately identifying maternal health risks through social network data is both vital and
challenging due to the complexities of multimodal sentiment analysis. Our study addresses this challenge by introducing
the maternal health risk factor detection using deep learning approach (MHRFD-DLA), a novel framework that integrates
convolutional neural networks, long short-term memory networks, and attention mechanisms. This approach enhances
sentiment analysis and risk detection in maternal health, with the focus on critical areas such as prenatal care, mental
health, and nutrition. MHRFD-DLA utilizes multimodal data, including text and electrocardiogram (ECG) signals, offering
a comprehensive assessment of maternal health risks. Our model outperforms existing multimodal sentiment analysis
models, achieving an accuracy of 98.4%, a precision of 97.6%, a recall of 95.6%, and an F1 score of 98.4%. Through
performance evaluations, visualizations such as the confusion matrix and class distributions further validate its robustness.
The MHRFD-DLA model not only bridges significant gaps in current methodologies, but it also sets a new benchmark for
maternal health surveillance and intervention, demonstrating its practicality and effectiveness in real-world applications.
Keywords
multifaceted emotion analysis, social networks, maternal health risk factor detection, deep learning, hybrid approach