Open Access
| Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
|---|---|---|
| Article Number | 00047 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/bioconf/20249700047 | |
| Published online | 05 April 2024 | |
- Tsao, C. W., Aday, A. W., Almarzooq, Z. I., Alonso, A., Beaton, A. Z., Bittencourt, M. S., & American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. (2022). Heart disease and stroke statistics—2022 update: a report from the American Heart Association. Circulation, 145(8), e153–e639. [CrossRef] [PubMed] [Google Scholar]
- Chatzinikolaou, A., Tzikas, S., & Lavdaniti, M. (2021). Assessment of Quality of Life in Patients With Cardiovascular Disease Using the SF-36, MacNew, and EQ-5D-5L Questionnaires. Cureus, 13(9). [PubMed] [Google Scholar]
- Srinivasan, S., Gunasekaran, S., Mathivanan, S. K., M. B, B. A. M., Jayagopal, P., & Dalu, G. T. (2023). An active learning machine technique based prediction of cardiovascular heart disease from UCI-repository database. Scientific Reports, 13 (1), 13588. [CrossRef] [PubMed] [Google Scholar]
- Dai, H., Younis, A., Kong, J. D., Puce, L., Jabbour, G., Yuan, H., & Bragazzi, N. L. (2022). Big data in cardiology: state-of-art and future prospects. Frontiers in Cardiovascular Medicine, 9, 844296. [CrossRef] [PubMed] [Google Scholar]
- Huang, J. D., Wang, J., Ramsey, E., Leavey, G., Chico, T. J., & Condell, J. (2022). Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: a review. Sensors, 22 (20), 8002. [CrossRef] [PubMed] [Google Scholar]
- Ogunpola, A., Saeed, F., Basurra, S., Albarrak, A. M., & Qasem, S. N. (2024). Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases. Diagnostics, 14 (2), 144. [CrossRef] [PubMed] [Google Scholar]
- Sanyal, S., Das, D., Biswas, S. K., Chakraborty, M., & Purkayastha, B. (2022, May). Heart Disease Prediction Using Classification Models. In 2022 3rd International Conference for Emerging Technology (INCET) (pp. 1–6). [Google Scholar]
- Hossain, M. I., Maruf, M. H., Khan, M. A. R., Prity, F. S., Fatema, S., Ejaz, M. S., & Khan, M. A. S. (2023). Heart disease prediction using distinct artificial intelligence techniques: performance analysis and comparison. Iran Journal of Computer Science, 1–21. [Google Scholar]
- UCI heart disease data set Retrieved from http://archive.ics.uci.edu/ml/datasets/heart+disease (2018, September 26), (2024, January 15). [Google Scholar]
- Sen, S. K. (2017). Predicting and diagnosing of heart disease using machine learning algorithms. International Journal of Engineering and Computer Science, 6(6), 21623–21631. [Google Scholar]
- Khan, S. (2017). Prediction of Angiographic Disease Status using Rule Based Data Mining Techniques Prediction of Angiographic Disease Status using Rule Based Data Mining Techniques. 8. [Google Scholar]
- Das, R., Turkoglu, I., & Sengur, A. (2009). Effective diagnosis of heart disease through neural networks ensembles. Expert systems with applications, 36(4), 7675–7680. [CrossRef] [Google Scholar]
- Amma, N. B. (2012, February). Cardiovascular disease prediction system using genetic algorithm and neural network. In 2012 international conference on computing, communication and applications (pp. 1–5). [Google Scholar]
- Santhanam, T., & Ephzibah, E. P. (2013). Heart disease classification using PCA and feed forward neural networks. In Mining Intelligence and Knowledge Exploration: First International Conference, MIKE 2013, Tamil Nadu, India, December 18-20, 2013. Proceedings (pp. 90–99). [Google Scholar]
- Chadha, R., & Mayank, S. (2016). Prediction of heart disease using data mining techniques. CSI transactions on ICT, 4, 193–198. [CrossRef] [Google Scholar]
- Shah, D., Patel, S., & Bharti, S. K. (2020). Heart disease prediction using machine learning techniques. SN Computer Science, 1, 1–6. [CrossRef] [Google Scholar]
- Dwivedi, A. K. (2018). Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Computing and Applications, 29, 685–693. [CrossRef] [Google Scholar]
- Deepika, K., & Seema, S. (2016, July). Predictive analytics to prevent and control chronic diseases. In 2016 2nd international conference on applied and theoretical computing and communication technology (iCATccT) (pp. 381–386). [CrossRef] [Google Scholar]
- Parthiban, G., & Srivatsa, S. K. (2012). Applying machine learning methods in diagnosing heart disease for diabetic patients. International Journal of Applied Information Systems, 3(7), 25–30. [CrossRef] [Google Scholar]
- Vembandasamy, K., Sasipriya, R., & Deepa, E. (2015). Heart diseases detection using Naive Bayes algorithm. International Journal of Innovative Science, Engineering & Technology, 2(9), 441–444. [Google Scholar]
- Otoom, A. F., Abdallah, E. E., Kilani, Y., Kefaye, A., & Ashour, M. (2015). Effective diagnosis and monitoring of heart disease. International Journal of Software Engineering and Its Applications, 9(1), 143–156. [Google Scholar]
- Ali, L., Rahman, A., Khan, A., Zhou, M., Javeed, A., & Khan, J. A. (2019). An automated diagnostic system for heart disease prediction based on statistical model and optimally configured deep neural network. Ieee Access, 7, 34938–34945. [CrossRef] [Google Scholar]
- Sagir, A. M., & Sathasivam, S. (2017). A Novel Adaptive Neuro Fuzzy Inference System Based Classification Model for Heart Disease Prediction. Pertanika Journal of Science & Technology, 25(1). [Google Scholar]
- Narasimhan, B., & Malathi, A. (2019). Altered particle swarm optimization based attribute selection strategy with improved fuzzy Artificial Neural Network classifier for coronary artery heart disease risk prediction. Int. J. Adv. Res. Ideas Innov. Technol, 5, 1196–1203. [Google Scholar]
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