A deep learning based hybrid model for maternal health risk detection and multifaceted emotion analysis in social networks

• Autorzy: Geethanjali R. , Valarmathi A.

Identyfikatory

DOI

10.61822/amcs-2024-0038

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

multifaceted emotion analysis; social network; maternal health; risk factor detection; deep learning; hybrid approach

PL

analiza emocji; sieć społecznościowa; zdrowie matki; wykrywanie czynników ryzyka; uczenie głębokie; podejście hybrydowe

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2024
• Tom: Vol. 34, no. 4
• Strony: 565--577
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Geethanjali R.

• Faculty of Information and Communication Engineering, Anna University, UCE-BIT Campus, Mandaiyur, Tiruchirappalli 620 024, Chennai, India

autor

Valarmathi A.

• Department of Computer Applications, Anna University, UCE-BIT Campus, Mandaiyur, Tiruchirappalli 620 024, Chennai, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).