Xinning Han
ABSTRACT
Due to factors e.g., incorrect diet and exercise habits of modern people, the number of heart disease patients is rising yearly. It is extremely important to find a good method to try to predict the different types of heart diseases. Because it is so hard to distinguish the different types of heart disease. Considering this, the use of different models is important to predict the type of heart disease. Then this study is trying to find the suitable models to solve these problems. In this work, the different models were used, which are the combination of ANN and SVM to get the accuracy of the models. First, ANN was used to obtain the accuracy of the model. Next, the SVM and ANN were used together to predict the result. Also the use of ResNet, which solve the gradient disappearance problem. Besides, comparing the use of Naïve Bayes, the use of Random Forest is trying to make the model stable and reduce the risk of overfitting. Also, the PCA model did the feature extractor and emerge the new features. The methods achieve the accuracy is about 90% of the Random Forest. Also, the confusion matrix of the model was obtained. After using 10 epochs, the accuracy of the combination of SVM and ANN is about 90.56%.
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Index Terms
- Heart Disease Type Prediction Model Based on SVM-ANN
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