Probabilistic decision support system using machine learning techniques : A case study of Cardiovascular diseases

References

• Mehanović, D., Mašetić, Z., & Kečo, D. (2019, May). Prediction of Heart Diseases Using Majority Voting Ensemble Method. In International Conference on Medical and Biological Engineering (pp. 491-498). Springer, Cham.
   Google Scholar
• Atallah, R., & Al-Mousa, A. (2019, October). Heart Disease Detection Using Machine Learning Majority Voting Ensemble Method. In 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS) (pp. 1-6). IEEE.
   Google Scholar
• Ismaeel, S., Miri, A., & Chourishi, D. (2015, May). Using the extreme learning machine (elm) technique for heart disease diagnosis. In 2015 IEEE Canada International Humanitarian Technology Conference (IHTC2015) (pp. 1-3). IEEE.
   Google Scholar
• Latha, C. B. C., & Jeeva, S. C. (2019). Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Informatics in Medicine Unlocked, 16, 100203. doi: https://doi.org/10.1016/j.imu.2019.100203
   Google Scholar
• Raza, K. (2019). Improving the prediction accuracy of heart disease with ensemble learning and majority voting rule. In U-Healthcare Monitoring Systems (pp. 179-196). Academic Press.
   Google Scholar
• Gajowniczek, K., Grzegorczyk, I., Ząbkowski, T., & Bajaj, C. (2020). Weighted Random Forests to Improve Arrhythmia Classification. Electronics, 9(1), 99. doi: https://doi.org/10.3390/electronics9010099
   Web of Science ®Google Scholar
• Ayon, S. I., Islam, M. M., & Hossain, M. R. (2020). Coronary Artery Heart Disease Prediction: A Comparative Study of Computational Intelligence Techniques. IETE Journal of Research, 1-20. doi: https://doi.org/10.1080/03772063.2020.1713916
   Google Scholar
• Kotur-Stevuljevic, J., Memon, L., Stefanovic, A., Spasic, S., Spasojevic-Kalimanovska, V., Bogavac-Stanojevic, N., … & Zunic, G. (2007). Correlation of oxidative stress parameters and inflammatory markers in coronary artery disease patients. Clinical biochemistry, 40(3-4), 181-187. doi: https://doi.org/10.1016/j.clinbiochem.2006.09.007
   PubMed Web of Science ®Google Scholar
• Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7, 81542-81554. doi: https://doi.org/10.1109/ACCESS.2019.2923707
   Web of Science ®Google Scholar
• Patel, J., Tejal Upadhyay, D., & Patel, S. (2015). Heart disease prediction using machine learning and data mining technique. Heart Disease, 7(1), 129-137.
   Google Scholar
• Bashir, S., Khan, Z. S., Khan, F. H., Anjum, A., & Bashir, K. (2019, January). Improving Heart Disease Prediction Using Feature Selection Approaches. In 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST) (pp. 619-623). IEEE.
   Google Scholar
• Kamble, S. U., Jawanjal, V. S., Velapure, P. P., Jadhav, P. K., & Kadam, S. S. (2019). Heart Disease Prediction using Machine Learning Techniques. IJETT, 6(1).
   Google Scholar
• Abdar, M., Książek, W., Acharya, U. R., Tan, R. S., Makarenkov, V., & Pławiak, P. (2019). A new machine learning technique for an accurate diagnosis of coronary artery disease. Computer methods and programs in biomedicine, 179, 104992. doi: https://doi.org/10.1016/j.cmpb.2019.104992
   PubMed Web of Science ®Google Scholar
• Abdar, M., Książek, W., Acharya, U. R., Tan, R. S., Makarenkov, V., & Pławiak, P. (2019). A new machine learning technique for an accurate diagnosis of coronary artery disease. Computer methods and programs in biomedicine, 179, 104992. doi: https://doi.org/10.1016/j.cmpb.2019.104992
   PubMed Web of Science ®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 χ2 Statistical Model and Optimally Configured Deep Neural Network. IEEE Access, 7, 34938-34945. doi: https://doi.org/10.1109/ACCESS.2019.2904800
   Web of Science ®Google Scholar
• Zhong, J., Liu, R., & Chen, P. (2020). Identifying critical state of complex diseases by single-sample Kullback–Leibler divergence. BMC genomics, 21(1), 87. doi: https://doi.org/10.1186/s12864-020-6490-7
   PubMed Web of Science ®Google Scholar
• Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017). Disease prediction by machine learning over big data from healthcare communities. Ieee Access, 5, 8869-8879. doi: https://doi.org/10.1109/ACCESS.2017.2694446
   Web of Science ®Google Scholar
• Khan, Y., Qamar, U., Yousaf, N., & Khan, A. (2019, February). Machine Learning Techniques for Heart Disease Datasets: A Survey. In Proceedings of the 2019 11th International Conference on Machine Learning and Computing (pp. 27-35).
   Google Scholar
• Latha Banda, Karan Singh, Le Hoang Son, Mohamed Abdel-Basset, Pham Huy Thong, Hiep Xuan Huynh, David Taniar (2020), “Recommender Systems using Collaborative Tagging”, International Journal of Data Warehousing and Mining, July-September 2020, Volume 16 • Issue 3, pp 183-200, (SCIE, IF:098). DOI: https://doi.org/10.4018/IJDWM.2020070110.
   Web of Science ®Google Scholar
• Lei, Z., Sun, Y., Nanehkaran, Y. A., Yang, S., Islam, M. S., Lei, H., & Zhang, D. (2020). A novel data-driven robust framework based on machine learning and knowledge graph for disease classification. Future Generation Computer Systems, 102, 534-548. doi: https://doi.org/10.1016/j.future.2019.08.030
   Web of Science ®Google Scholar
• Gavhane, A., Kokkula, G., Pandya, I., & Devadkar, K. (2018, March). Prediction of heart disease using machine learning. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1275-1278). IEEE.
   Google Scholar
• Hatt, M., Parmar, C., Qi, J., & El Naqa, I. (2019). Machine (deep) learning methods for image processing and radiomics. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 104-108. doi: https://doi.org/10.1109/TRPMS.2019.2899538
   Web of Science ®Google Scholar
• Shankar, V. and Singh, K., 2019. An Intelligent Scheme for Continuous Authentication of Smartphone Using Deep Auto Encoder and Softmax Regression Model Easy for User Brain. IEEE Access, Vol. 7, pp.48645-48654, Electronic ISSN: 2169-3536, DOI: https://doi.org/10.1109/ACCESS.2019.2909536. IEEE Access, April 2019.
   Web of Science ®Google Scholar