Classification of Heart Disease Diagnosis using the Random Forest Algorithm
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Abstract
The heart is an important part of the human body organs. But it does not rule out this heart problem and causes several symptoms, causing deadly heart disease. Therefore, many studies are used to obtain fast, precise and accurate heart disease diagnosis data. One of them is the classification of heart disease diagnoses using the Random Forest algorithm. This random forest algorithm method uses several unified decision trees. So that accurate results can be obtained regarding the diagnosis of this heart disease. The accuracy obtained from the experimental results using the Python programming language is 85.3%.
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References
H. Bagheri and A. A. Shaltooki, “Big data: Challenges, opportunities and cloud based solutions,†Int. J. Electr. Comput. Eng., vol. 5, no. 2, pp. 340–343, 2015, doi: 10.11591/ijece.v5i2.pp340-343.
V. Paramasivam, T. S. Yee, S. K. Dhillon, and A. S. Sidhu, “A methodological review of data mining techniques in predictive medicine: An application in hemodynamic prediction for abdominal aortic aneurysm disease,†Biocybernetics and Biomedical Engineering, vol. 34, no. 3. pp. 139–145, 2014, doi: 10.1016/j.bbe.2014.03.003.
K. C. Tan, E. J. Teoh, Q. Yu, and K. C. Goh, “A hybrid evolutionary algorithm for attribute selection in data mining,†Expert Syst. Appl., vol. 36, no. 4, pp. 8616–8630, 2009, doi: 10.1016/j.eswa.2008.10.013.
S. Benbelkacem and B. Atmani, “Random forests for diabetes diagnosis,†in 2019 International Conference on Computer and Information Sciences, ICCIS 2019, 2019, doi: 10.1109/ICCISci.2019.8716405.
A. Subas, E. Alickovic, and J. Kevric, “Diagnosis of chronic kidney disease by using random forest,†in IFMBE Proceedings, 2017, vol. 62, pp. 589–594, doi: 10.1007/978-981-10-4166-2_89.
G. Sun, S. Li, Y. Cao, and F. Lang, “Cervical cancer diagnosis based on random forest,†Int. J. Performability Eng., vol. 13, no. 4, pp. 446–457, 2017, doi: 10.23940/ijpe.17.04.p12.446457.
M. Govardhan and M. B. Veena, “Diagnosis of Tomato Plant Diseases using Random Forest,†in 2019 Global Conference for Advancement in Technology, GCAT 2019, 2019, doi: 10.1109/GCAT47503.2019.8978431.
L. Breiman, “Random forests,†UC Berkeley TR567, 1999.
T. K. Ho, “Random decision forests,†in Proceedings of 3rd international conference on document analysis and recognition, 1995, vol. 1, pp. 278–282.
S. Polamuri, “How the random forest algorithm works in machine learning,†Retrieved December, vol. 21, 2017.
Y. L. Pavlov, Random forests. Walter de Gruyter GmbH & Co KG, 2019.