Classification and Prediction of Cardiovascular Patients Based on Optimal Random Forest Algorithm
DOI: 10.23977/csoc.2022.020104 | Downloads: 23 | Views: 1914
Author(s)
Jiaqi Huang 1, Mingguang Li 2
Affiliation(s)
1 School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430000, China
2 School of Computing, Nantong University of Science and Technology, Jiangsu, 210000, China
Corresponding Author
Jiaqi HuangABSTRACT
Cardiovascular disease is a high-risk disease and therefore machine learning is needed to classify and predict it in order to aid research in the medical field. A prediction model for classifying cardiovascular patients based on an optimised random forest algorithm and comparing the prediction performance of each model. Using publicly available data on cardiovascular disease from the Kaggle platform, classification prediction models for cardiovascular disease were developed based on an integrated learning approach using Random Forest, Parsimonious Bayes, SVM and AdaBoost algorithms based on 12 indicators that may have an impact on the mortality of patients with cardiovascular disease. and classification prediction effects. Using the multiple averaging method to ensure the accuracy of the algorithms, the four types of AUROC values were observed and visualisation using matlab's powerful toolbox yielded the best ROC curve fit for random forest with an AUC value of 0.90.
KEYWORDS
Cardiovascular disease, integrated learning, optimal random forest, machine learningCITE THIS PAPER
Jiaqi Huang, Mingguang Li, Classification and Prediction of Cardiovascular Patients Based on Optimal Random Forest Algorithm. Cloud and Service-Oriented Computing (2022) Vol. 2: 28-35. DOI: http://dx.doi.org/10.23977/csoc.2022.020104.
REFERENCES
[1] Iwendi, Celestine, et al. "COVID-19 patient health prediction using boosted random forest algorithm." Frontiers in public health 8 (2020): 357.
[2] Cornelius, Erwin, Olcay Akman, and Dan Hrozencik. "COVID-19 mortality prediction using machine learning-integrated Random Forest algorithm under varying patient frailty." Mathematics 9.17 (2021): 2043.
[3] Ward, Michael M., et al. "Short-term prediction of mortality in patients with systemic lupus erythematosus: Classification of Arthritis Care & Research: Official Journal of the American College of Rheumatology 55.1 (2006): 74-80.
[4] Nahar, Nazmun, and Ferdous Ara. "Liver disease prediction by using different decision tree techniques. "International Journal of Data Mining & Knowledge Management Process 8.2 (2018): 01-09.
[5] Jabbar, M. A., and Shirina Samreen. "Heart disease prediction system based on hidden naïve bayes classifier. "2016 International Conference on Circuits, Controls, Communications and Computing (I4C). IEEE, 2016.
[6] Lin Y, Wu JY, Lin K, Hu YH, Kong KWL. Predicting the risk of readmission of critically ill patients to intensive care units based on integrated learning models [J]. Journal of Peking University (Medical Edition),2021,53(03):566-572.
[7] Jiang Xingli, Wang Jianhui. Disease classification prediction based on decision tree algorithm [J]. Information and Computers (Theoretical Edition), 2021, 33 (11): 51-53.
[8] Yang, Li, et al. "Study of cardiovascular disease prediction model based on random forest in eastern China." Scientific reports 10.1 (2020): 1-8.
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