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An Efficient Machine Learning-Based Decision-Level Fusion Model to Predict Cardiovascular Disease

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1324)


The world’s primary cause of mortality is cardiovascular disease at present. Identifying the risk early could reduce the rate of death. Sometimes, it is difficult for a person to undergo an expensive test regularly. So, there should be a system that can predict the presence of cardiovascular disease by analyzing the basic symptoms. Researchers have focused on building machine learning-based prediction systems to make the process more simple and efficient and reduce both doctors’ and patients’ burdens. In this paper, a decision level fusion model is designed to predict cardiovascular disease with the help of machine learning algorithms that are multilayer neural network and the K Nearest Neighbor (KNN). The decision of each model was merged for the final decision to improves the accuracy. Here Cleveland dataset was used for ANN and KNN, which contains the information of 303 patients with eight attributes. In this two-class classification, ANN gave 92.10% accuracy, and KNN gave 88.16%. After fusing the decision of them, we got an accuracy of 93.42% that performed much better than two of them. The result was obtained by using 75% data in training.


  • Cardiovascular disease
  • Machine learning
  • Artificial neural network
  • knn
  • Decision level fusion

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  • DOI: 10.1007/978-3-030-68154-8_92
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Correspondence to Hafsa Binte Kibria .

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Kibria , H.B., Matin, A. (2021). An Efficient Machine Learning-Based Decision-Level Fusion Model to Predict Cardiovascular Disease. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham.

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