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27 September 2022 An SVM CHD prediction study with optimal parameters
Honggang Huang, Rui Zhang, Qingquan Jiang
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Proceedings Volume 12345, International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2022); 1234512 (2022) https://doi.org/10.1117/12.2648901
Event: 2022 International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2022), 2022, Qingdao, China
Abstract
In recent years, data mining algorithms have been widely recognized as an important way to predict diseases. In the medical field, we are often faced with massive data. How to choose a model and improve the model to obtain better results is a problem that researchers are committed to solving. This paper starts with the classification model of Support Vector Machine, and optimizes the its parameters by processing the data. We used the previous Genetic Algorithm for comparing, and proposed an improved Genetic Algorithm to optimize the prediction of Support Vector Machine on the Coronary Heart Disease dataset. The experimental results show that the improved Genetic Algorithm has better prediction accuracy than the previous Genetic Algorithm.
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Honggang Huang, Rui Zhang, and Qingquan Jiang "An SVM CHD prediction study with optimal parameters", Proc. SPIE 12345, International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2022), 1234512 (27 September 2022); https://doi.org/10.1117/12.2648901
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KEYWORDS
Genetic algorithms
Heart
Data processing
Machine learning
Optimization (mathematics)
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