Predicting Cardiovascular Disease Risk Using Web-Text Sentiment Analysis and Hybrid Deep Learning Models
Abhijeet Madhukar Haval 1*, Md Afzal 1
Journal of Angiotherapy 8(9) 1-6 https://doi.org/10.25163/angiotherapy.899876
Submitted: 05 September 2024 Revised: 29 August 2024 Published: 08 September 2024
Abstract
Background: Sentiment Analysis (SA) has emerged as a key tool within Natural Language Processing (NLP) for quantifying emotions expressed in Web Text (WT). Its application in healthcare, particularly in predicting cardiovascular diseases (CD), shows promise. Sentiments related to stress, anger, or other emotional states have been linked to increased CD risk, making Web-Text Sentiment Analysis (WT-SA) a valuable tool in public health prediction. This study compares WT-SA to demographic data from the Centers for Disease Control and Prevention (CDC) in predicting CD risks. Methods: Twitter data was analyzed using SA techniques to assess sentiments related to CD. A hybrid deep learning (DL) model combining 3D-Convolutional Neural Networks (3D-CNN) and Recurrent Neural Networks (RNN) was utilized alongside traditional machine learning (ML) models: Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), and CatBoost. The WT data, spanning three years (2020-2023), was split into training (75%) and testing (25%) datasets. Model performance was evaluated using Accuracy, Precision, Recall, and F1 score. Results: The hybrid 3D-CNN+RNN model achieved the highest test accuracy of 0.95 on Twitter data, outperforming all other models. SVM, LR, NB, and CatBoost achieved accuracies of 0.89, 0.88, 0.75, and 0.77, respectively. When applied to the CDC dataset, the hybrid model reached an accuracy of 0.678, still outperforming the other models. Overall, the WT-SA model demonstrated superior performance compared to using demographic data alone. Conclusion: WT-SA, when combined with hybrid DL models, is a more effective method for predicting CD risk than demographic-based models. This study determined the potential of NLP and DL techniques in leveraging social media data for public health monitoring, suggesting that WT-SA could be a valuable tool for predicting CD risks and enhancing early intervention efforts.
Keywords: Sentiment Analysis, Cardiovascular diseases, Web-Text, Convolution Neural Network, Recurrent Neural Network, Natural Language Processing.
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