Predicting Cardiovascular Disease Risk Using Web-Text Sentiment Analysis and Hybrid Deep Learning Models

Abhijeet Madhukar Haval ¹*, Md Afzal ¹

This study demonstrated the superior potential of Web-Text Sentiment Analysis in predicting cardiovascular disease over demographic-based models.

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.

References

Brezulianu, A., Burlacu, A., Popa, I. V., Arif, M., & Geman, O. (2022). "Not by our feeling, but by others' seeing": Sentiment analysis technique in cardiology—An exploratory review. Frontiers in Public Health, 10, 880207.

Briganti, G., & Le Moine, O. (2020). Artificial intelligence in medicine: Today and tomorrow. Frontiers in Medicine, 7, 509744.

Eberly, L. A., Khatana, S. A. M., Nathan, A. S., Snider, C., Julien, H. M., Deleener, M. E., & Adusumalli, S. (2020). Telemedicine outpatient cardiovascular care during the COVID-19 pandemic: Bridging or opening the digital divide?. Circulation, 142(5), 510-512.

Elbagir, S., & Yang, J. (2019, March). Twitter sentiment analysis using natural language toolkit and VADER sentiment. In Proceedings of the international multiconference of engineers and computer scientists (Vol. 122, No. 16). sn.

Fan, B., Fan, W., & Smith, C. (2020). Adverse drug event detection and extraction from open data: A deep learning approach. Information Processing & Management, 57(1), 102131.

Gohil, S., Vuik, S., & Darzi, A. (2018). Sentiment analysis of health care tweets: Review of the methods used. JMIR Public Health and Surveillance, 4(2), e5789.

Gümüs, A. E., Uyulan, Ç., & Guleken, Z. (2022). Detection of EEG patterns for induced fear emotion state via EMOTIV EEG testbench. Natural and Engineering Sciences, 7(2), 148-168.

Huang, D., Huang, Y., Adams, N., Nguyen, T. T., & Nguyen, Q. C. (2020). Twitter-characterized sentiment towards racial/ethnic minorities and cardiovascular disease (CD) outcomes. Journal of Racial and Ethnic Health Disparities, 7(5), 888-900.

Laranjo, L., Lanas, F., Sun, M. C., Chen, D. A., Hynes, L., Imran, T. F., ... & Chow, C. K. (2024). World Heart Federation roadmap for secondary prevention of cardiovascular disease: 2023 update. Global Heart, 19(1).

Lavanya, P., Subba, R. I. V., Selvakumar, V., & Deshpande, S. V. (2024). An intelligent health surveillance system: Predictive modeling of cardiovascular parameters through machine learning algorithms using LoRa communication and the Internet of Medical Things (IoMT). Journal of Internet Services and Information Security, 14(1), 165-179.

Ligthart, A., Catal, C., & Tekinerdogan, B. (2021). Systematic reviews in sentiment analysis: A tertiary study. Artificial Intelligence Review, 1-57.

Mahadevkar, S. V., Patil, S., Kotecha, K., Soong, L. W., & Choudhury, T. (2024). Exploring AI-driven approaches for unstructured document analysis and future horizons. Journal of Big Data, 11(1), 92.

Mumtaj Begum, H. (2022). Scientometric analysis of the research paper output on artificial intelligence: A study. Indian Journal of Information Sources and Services, 12(1), 52–58.

Reference

Sathyanarayanan, S., & Srikanta, M. K. (2024). Heart sound analysis using SAINet incorporating CNN and transfer learning for detecting heart diseases. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 15(2), 152-169. https://doi.org/10.58346/JOWUA.2024.I2.011

Toshtemirovna, E. M. M., Alisherovna, K. M., Totlibayevich, Y. S., & Xudoyberdiyevich, G. X. (2022). Anxiety disorders and coronary heart disease. The Peerian Journal, 11, 58-63.

Tweepy. (2024). Retrieved from https://docs.tweepy.org/en/stable/

Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731-5780.

Yazdani, A., Shamloo, M., Khaki, M., & Nahvijou, A. (2023). Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language. BMC Medical Informatics and Decision Making, 23(1), 275.