1
Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia
(alkattan.hussein92@gmail.com)
2
Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
(sktowfek@jcsis.org)
3
Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt
(mahmoud.yasin@ai.kfs.edu.eg)
Abstract :
The prevalence of cardiovascular disease (CVD) is a serious public health issue, and it is of particular concern for people with diabetes because of the increased risk of cardiovascular problems that these people experience. In this study, we suggest a novel method of Ontological Data Mining (ODM) for identifying the origins of CVD risk in diabetic patients. We want to improve the readability and precision of prediction models by incorporating domain knowledge and semantic linkages into the data mining process. In this work, we examine a large dataset consisting of 70,000 patient records with 11 attributes, all of which are derived through a thorough clinical history and physical examination. As part of our methodology, we use decision trees, support vector machines (SVMs), and gradient boosting (GB). The distribution patterns of critical variables with respect to CVD outcomes can be better understood through the use of visual representations such as box plots, distributional plots, and pie charts. Finding significant connections and causal relationships between risk factors and CVD outcomes is made possible by the suggested ODM method. Our research has promising implications for bettering the treatment of patients with diabetes, facilitating targeted interventions, and enhancing risk assessment and preventative methods for cardiovascular disease.
Keywords :
Ontological Data Mining; Cardiovascular Disease; Diabetes; Boosting; Predictive Models; Interpretability; Data Visualization.
References :
[1] Jayaraman P. P., Forkan A. R. M., Morshed A., Haghighi P. D., Kang Y. B., Healthcare 4.0: A review of frontiers in digital health. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(2), 2020.
[2] Rahman M. F., Wen Y., Xu H., Tseng T. L. B., Akundi S., Data mining in telemedicine, Advances in Telemedicine for Health Monitoring: Technologies, Design and Applications, 103-131, 2020.
[3] Aseel M. Alfaisal, Aisha Zare, Afrah Alshaafi, Rose Aljanada, Raghad M. Alfaisal, Ghadeer W. Abukhalil, Predicting the actual use of social media sites among university communicators: using PLS-SEM and ML approaches. Journal of International Journal of Advances in Applied Computational Intelligence, 1(1), 23-33, 2022.
[4] Mohamed Saber, Efficient phase recovery system, IJEECS, 5(1), 2017.
[5] Raghad M. Alfaisal, Aisha Zare, Aseel M. Alfaisal, Rose Aljanada, Ghadeer Wael Abukhalil, The acceptance of metaverse system: a hybrid SEM-ML approach. Journal of International Journal of Advances in Applied Computational Intelligence, 1(1), 34-44, 2022.
[6] Heba R. Abdelhady, Mahmoud M. Ismail, Cardiovascular Diseases Forecasting using Machine Learning Models. Journal of International Journal of Advances in Applied Computational Intelligence, 1(2), 56-62, 2022.
[7] Rose Aljanada, Ghadeer W. Abukhalil, Aseel M. Alfaisal, Raghad M. Alfaisal, Adoption of Google Glass technology: PLS-SEM and machine learning analysis. Journal of International Journal of Advances in Applied Computational Intelligence, 1 (1), 08-22, 2022.
[8] Silverman E. K., Schmidt H. H., Anastasiadou E., Altucci L., Angelini M., Badimon L., Baumbach J., Molecular networks in Network Medicine: Development and applications. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 12(6), 2020.
[9] Hani D. Hejazi, Ahmed A. Khamees, Employees Motivational Factors toward Knowledge Sharing: A Systematic Review, Journal of International Journal of Advances in Applied Computational Intelligence, 1(1), 45-68, 2022.
[10] Ahmed Z., Mohamed K., Zeeshan S., Dong, X., Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020.
[11] Aseel M. Alfaisal, Aisha Zare, Afrah Alshaafi, Rose Aljanada, Raghad M. Alfaisal, Ghadeer W. Abukhalil, Predicting the actual use of social media sites among university communicators: using PLS-SEM and ML approaches. Journal of International Journal of Advances in Applied Computational Intelligence, 1 (1), 23-33, 2022.
[12] Sulaiman M. A., Evaluating data mining classification methods performance in Internet of things applications. Journal of Soft Computing and Data Mining, 1(2), 11-25, 2020.
[13] Ye M. (2011). Text mining for building a biomedical knowledge base on diseases, risk factors, and symptoms. Germany: Max-Planck-Institute for Informatics.
[14] Abdulla Alsharhan, Natural Language Generation and Creative Writing A Systematic Review. Journal of International Journal of Advances in Applied Computational Intelligence, 1(1) , 69-90, 2022.
[15] Mohamed Saber, A novel design and implementation of FBMC transceiver for low power applications. IJEEI, 8(1), 83-93, 2020.
[16] Raghad M. Alfaisal,Aisha Zare,Aseel M. Alfaisal,Rose Aljanada,Ghadeer Wael Abukhalil, The acceptance of metaverse system: a hybrid SEM-ML approach. Journal of International Journal of Advances in Applied Computational Intelligence, 1 (1), 34-44, 2022.
[17] Wu X., Duan, J., Pan Y., Li M., Medical knowledge graph: Data sources, construction, reasoning, and applications. Big Data Mining and Analytics, 6(2), 201-217, 2023.
[18] Groza T., Köhler S., Moldenhauer D., Vasilevsky N., Baynam G., Zemojtel T., Robinson P. N., The human phenotype ontology: semantic unification of common and rare disease. The American Journal of Human Genetics, 97(1), 111-124, 2015.
[19] Ismail Eyad Samara, Intelligent systems and AI techniques: Recent advances and Future directions. Journal of International Journal of Advances in Applied Computational Intelligence, 1(2) , 30-45, 2022.
[20] Hani D. Hejazi , Ahmed A. Khamees, Employees Motivational Factors toward Knowledge Sharing: A Systematic Review. Journal of International Journal of Advances in Applied Computational Intelligence, 1 (1), 45-68, 2022.
[21] Alber S. Aziz, Hoda K. Mohamed, Ahmed Abdelhafeez, Unveiling the Power of Convolutional Networks: Applied Computational Intelligence for Arrhythmia Detection from ECG Signals." Journal of International Journal of Advances in Applied Computational Intelligence, 1(2), 63-72, 2022.
| Style | # |
|---|---|
| MLA | Hussein Alkattan, S. K. Towfek, M. Y. Shams. "Tapping into Knowledge: Ontological Data Mining Approach for Detecting Cardiovascular Disease Risk Causes Among Diabetes Patients." Journal of Artificial Intelligence and Metaheuristics, Vol. 4, No. 1, 2023 ,PP. 08-15 (Doi : https://doi.org/10.54216/JAIM.040101) |
| APA | Hussein Alkattan, S. K. Towfek, M. Y. Shams. (2023). Tapping into Knowledge: Ontological Data Mining Approach for Detecting Cardiovascular Disease Risk Causes Among Diabetes Patients. Journal of Journal of Artificial Intelligence and Metaheuristics, 4 ( 1 ), 08-15 (Doi : https://doi.org/10.54216/JAIM.040101) |
| Chicago | Hussein Alkattan, S. K. Towfek, M. Y. Shams. "Tapping into Knowledge: Ontological Data Mining Approach for Detecting Cardiovascular Disease Risk Causes Among Diabetes Patients." Journal of Journal of Artificial Intelligence and Metaheuristics, 4 no. 1 (2023): 08-15 (Doi : https://doi.org/10.54216/JAIM.040101) |
| Harvard | Hussein Alkattan, S. K. Towfek, M. Y. Shams. (2023). Tapping into Knowledge: Ontological Data Mining Approach for Detecting Cardiovascular Disease Risk Causes Among Diabetes Patients. Journal of Journal of Artificial Intelligence and Metaheuristics, 4 ( 1 ), 08-15 (Doi : https://doi.org/10.54216/JAIM.040101) |
| Vancouver | Hussein Alkattan, S. K. Towfek, M. Y. Shams. Tapping into Knowledge: Ontological Data Mining Approach for Detecting Cardiovascular Disease Risk Causes Among Diabetes Patients. Journal of Journal of Artificial Intelligence and Metaheuristics, (2023); 4 ( 1 ): 08-15 (Doi : https://doi.org/10.54216/JAIM.040101) |
| IEEE | Hussein Alkattan, S. K. Towfek, M. Y. Shams, Tapping into Knowledge: Ontological Data Mining Approach for Detecting Cardiovascular Disease Risk Causes Among Diabetes Patients, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 4 , No. 1 , (2023) : 08-15 (Doi : https://doi.org/10.54216/JAIM.040101) |