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- Vol. 7 Issue 6
- Article
AI-Driven Heart Disease Prediction System Using Natural Language Processing and Machine Learning
Authors
1 2 Assistant Professor, Department of Computer Science and Engineering, Kalpataru Institute of Technology, Tiptur, Karnataka, India. 3 4 5 Department of Computer Science and Engineering, Kalpataru Institute of Technology, Tiptur, Karnataka, India.
Article Information
- Published In
- Volume 7, Issue 6 (2026)
- Published Online
- November-December 2026
- Pages
- 34-43
- Article Type
- Original Article
References
[1] A. A. Saputra, A. I. Nurgina, K. Ma’ruf, R. J. Setiawan, and N. Azizah, “Heart disease prediction using decision tree classification:
A Streamlit-based clinical tool for early detection,” in 2025 6th Int. Conf. Data Analytics for Business and Industry (ICDABI), 2025,
pp. 259–264.
[2] S. Ambekar and R. Phalnikar, “Disease risk prediction by using convolutional neural network,” in 2018 4th Int. Conf. Computing
Communication Control and Automation (ICCUBEA), 2018.
[3] G. Sharma, D. Kulshrestha, A. Upadhyay, and V. Chaudhary, “IoT-enabled heart disease risk prediction using machine learning
algorithms: A comparative analysis of ML models,” in 2025 IEEE Guwahati Subsection Conf. (GCON), 2025.
[4] K. Balasaravanan, G. S. Gopika, and M. Sreekrishna, “Streamlit-based multi-disease prediction system for early detection of kidney
disease, breast cancer, and heart disease,” in 2025 IEEE Int. Conf. Contemporary Computing and Communications (InC4), 2025.
[5] M. T. Islam, S. R. Rafa, and M. G. Kibria, “Early prediction of heart disease using PCA and hybrid genetic algorithm with k-means,”
in 2020 23rd Int. Conf. Computer and Information Technology (ICCIT), 2020.
[6] T. Soni, D. Gupta, and M. Uppal, “Employing XGBoost algorithm for accurate prediction of cardiovascular disease and its risk
factors,” in 2024 IEEE 3rd World Conf. Applied Intelligence and Computing (AIC), 2024, pp. 353–358.
[7] V. M. Vibha, S. R. Sneha, U. Kiran, and Y. Kirana, “Exploratory data analysis of heart disease prediction using machine learning
techniques - RS algorithm,” in 2024 2nd Int. Conf. Intelligent Cyber Physical Systems and Internet of Things (ICoICI), 2024, pp.
209–216.
[8] M. A. Rabbi, M. R. H. Rijon, S. S. Akhi, A. Hossain, and S. M. Jeba, “A detailed analysis of machine learning algorithm performance
in heart disease prediction,” in 2025 4th Int. Conf. Robotics, Electrical and Signal Processing Techniques (ICREST), 2025, pp. 259–
263.
[9] A. Q. Saeed, R. F. N. Alshammari, and B. M. M. H., “Early prediction of heart disease using machine learning,” in 2025 3rd Int.
Conf. Business Analytics for Technology and Security (ICBATS), 2025.
[10] V. Srija, S. U. R. K. Chavali, S. Kiranmai, P. S. Kumar, G. S. Reddy, and V. Bhanu, “Heart disease prediction system using machine
learning,” in 2025 3rd World Conf. Communication and Computing (WCONF), 2025.
[11] S. Riyadi, M. I. Al-Habib, F. Alfiana, C. Damarjati, Y. Jusman, and S. Ramli, “Early detection of coronary heart disease using
exploration data analysis and gradient boosting method,” in 2024 Int. Conf. Information Technology and Computing (ICITCOM),
2024, pp. 42–47.
[12] M. Hemalatha, R. G. Anuja, and S. Monisha, “Predicting heart disease risk using the XGBoost algorithm,” in 5th Int. Conf.
Evolutionary Computing and Mobile Sustainable Networks (ICECMSN), 2025, pp. 6–11.
[13] A. Azarnik, A. Khosravi, and A. Moradi, “Heart disease prediction: A novel approach using mutual information and deep learning,”
in 2025 11th Int. Conf. Signal Processing and Intelligent Systems (ICSPIS), 2025, pp. 114–116.
[14] Ullas H S, Suresh M, and Shakunthala B S, “Heart disease prediction using machine learning,” Grenze Int. J. Eng. Technol., June
issue, 2025.
A Streamlit-based clinical tool for early detection,” in 2025 6th Int. Conf. Data Analytics for Business and Industry (ICDABI), 2025,
pp. 259–264.
[2] S. Ambekar and R. Phalnikar, “Disease risk prediction by using convolutional neural network,” in 2018 4th Int. Conf. Computing
Communication Control and Automation (ICCUBEA), 2018.
[3] G. Sharma, D. Kulshrestha, A. Upadhyay, and V. Chaudhary, “IoT-enabled heart disease risk prediction using machine learning
algorithms: A comparative analysis of ML models,” in 2025 IEEE Guwahati Subsection Conf. (GCON), 2025.
[4] K. Balasaravanan, G. S. Gopika, and M. Sreekrishna, “Streamlit-based multi-disease prediction system for early detection of kidney
disease, breast cancer, and heart disease,” in 2025 IEEE Int. Conf. Contemporary Computing and Communications (InC4), 2025.
[5] M. T. Islam, S. R. Rafa, and M. G. Kibria, “Early prediction of heart disease using PCA and hybrid genetic algorithm with k-means,”
in 2020 23rd Int. Conf. Computer and Information Technology (ICCIT), 2020.
[6] T. Soni, D. Gupta, and M. Uppal, “Employing XGBoost algorithm for accurate prediction of cardiovascular disease and its risk
factors,” in 2024 IEEE 3rd World Conf. Applied Intelligence and Computing (AIC), 2024, pp. 353–358.
[7] V. M. Vibha, S. R. Sneha, U. Kiran, and Y. Kirana, “Exploratory data analysis of heart disease prediction using machine learning
techniques - RS algorithm,” in 2024 2nd Int. Conf. Intelligent Cyber Physical Systems and Internet of Things (ICoICI), 2024, pp.
209–216.
[8] M. A. Rabbi, M. R. H. Rijon, S. S. Akhi, A. Hossain, and S. M. Jeba, “A detailed analysis of machine learning algorithm performance
in heart disease prediction,” in 2025 4th Int. Conf. Robotics, Electrical and Signal Processing Techniques (ICREST), 2025, pp. 259–
263.
[9] A. Q. Saeed, R. F. N. Alshammari, and B. M. M. H., “Early prediction of heart disease using machine learning,” in 2025 3rd Int.
Conf. Business Analytics for Technology and Security (ICBATS), 2025.
[10] V. Srija, S. U. R. K. Chavali, S. Kiranmai, P. S. Kumar, G. S. Reddy, and V. Bhanu, “Heart disease prediction system using machine
learning,” in 2025 3rd World Conf. Communication and Computing (WCONF), 2025.
[11] S. Riyadi, M. I. Al-Habib, F. Alfiana, C. Damarjati, Y. Jusman, and S. Ramli, “Early detection of coronary heart disease using
exploration data analysis and gradient boosting method,” in 2024 Int. Conf. Information Technology and Computing (ICITCOM),
2024, pp. 42–47.
[12] M. Hemalatha, R. G. Anuja, and S. Monisha, “Predicting heart disease risk using the XGBoost algorithm,” in 5th Int. Conf.
Evolutionary Computing and Mobile Sustainable Networks (ICECMSN), 2025, pp. 6–11.
[13] A. Azarnik, A. Khosravi, and A. Moradi, “Heart disease prediction: A novel approach using mutual information and deep learning,”
in 2025 11th Int. Conf. Signal Processing and Intelligent Systems (ICSPIS), 2025, pp. 114–116.
[14] Ullas H S, Suresh M, and Shakunthala B S, “Heart disease prediction using machine learning,” Grenze Int. J. Eng. Technol., June
issue, 2025.
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