ITM Web Conf.
Volume 44, 2022International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
|Number of page(s)||6|
|Published online||05 May 2022|
- A.S. Monto, S. Gravenstein, M. Elliott, M. Colopy, J. Schweinle, Clinical signs and symptoms predicting inuenza infection, Archives of internal medicine 160(21), 3243 (2000) [CrossRef] [Google Scholar]
- B. Qian, X. Wang, N. Cao, H. Li, and Y.-G. Jiang, “A relative similarity based method for interactive patient risk prediction, ” Springer Data Mining Knowl. Discovery, vol. 29, no. 4, pp. 1070–1093, 2015. [CrossRef] [Google Scholar]
- D.R. Langbehn, R.R. Brinkman, D. Falush, J.S. Paulsen, M. Hayden, an International Huntington’s Disease Collaborative Group, A new model for prediction of the age of onset and penetrance for huntington’s disease based on cag length, Clinical genetics 65(4), 267 (2004) [CrossRef] [Google Scholar]
- S. Grampurohit and C. Sagarnal, ”Disease Prediction using Machine Learning Algorithms,” 2020 International Conference for Emerging Technology(INCET), 2020, pp. 17, DOI: 10.1109/INCET49848.2020.9154130. [Google Scholar]
- D. Dahiwade, G. Patle and E. Meshram, “Designing Disease Prediction Model Using Machine Learning Approach,” 2019 3rd International Con- ference on Computing Methodologies and Communication (ICCMC), 2019, pp. 1211–1215, DOI: 10.1109/ICCMC.2019.8819782. [CrossRef] [Google Scholar]
- Automatic Heart Disease Prediction Using Feature Selection And Data Mining Technique Le Ming Hung a Tran Ding, Journal of Computer Science and Cybernetics, V. 34, N. 1 (2018), 3347 DOI: 10.15625/1813-9663/34/1/12665 [Google Scholar]
- International Journal of Scientific Research in Computer Science, E., & IJSRCSEIT, I. T. (2019). Generic Disease Prediction using Symptoms with Supervised Machine Learning. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/CSEIT1952297. [Google Scholar]
- Pingale, Kedar, et al. “Disease Prediction using Machine Learning.” (2019). Mr. Chala Beyene, Prof. Pooja Kamat, “Survey on Prediction and Analysis the Occurrence of Heart Disease Using Data Mining Techniques”, International Journal of Pure and Applied Mathematics, 2018. [Google Scholar]
- S. Patel and H. Patel, “Survey of data mining techniques used in healthcare domain, ” Int. J. of Inform. Sci. and Tech., Vol. 6, pp. 53–60, March, 2016. [Google Scholar]
- Balasubramanian, Satyabhama, and Balaji Subramanian. “Symptom based disease prediction in medical system by using Kmeans algorithm.” International Journal of Advances in Computer Science and Technology 3. [Google Scholar]
- Dhenakaran, K. Rajalakshmi S.S. “Analysis of Data mining Prediction Techniques in Healthcare Management System.” International Journal of Advanced Research in Computer Science and Software Engineering 5.4 (2015). [Google Scholar]
- T. Vivekanandan and N. C. S. N. Iyengar, “Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease,” Comput. Biol. Med., vol. 90, pp. 125–136, Nov. 2017. [CrossRef] [Google Scholar]
- A. Gavhane, G. Kokkula, I. Pandya, and K. Devadkar, “Prediction of heart disease using machine learning,” in Proc. 2nd Int. Conf. Electron., Commun. Aerosp. Technol. (ICECA), Mar. 2018, pp. 1275–1127 [Google Scholar]
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