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Towards Building a Global Robust Model for Heart Disease Detection

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Abstract

Heart disease is one of the most menacing non-communicable diseases. Heart disease prediction is one of the most popular topics in the field of biomedical engineering. Almost all the models for heart disease detection are based on centralized training of data where data are concentrated in a single server. Centralized training of the model may give a good accuracy to predict/detect, yet the models are not robust as only a single dataset is used to train and test the model. This is a major problem with centralized training. Therefore, in order to solve this problem of robustness, we decided to use and simulate federated learning (FL) to build a heart disease detection model through which we can use multiple datasets from different regions to train the model. We created four virtual workers where each one is representing a hospital present in a different location containing the local data. We have used the dataset downloaded from IEEE Dataport. It is a combination of five different datasets, including the Cleveland, Hungarian, Switzerland, Long Beach VA, and Statlog Heart Dataset. The final dataset contains 11 input features and the output label. We divided the dataset into training and testing data. The training data are divided into four sub-datasets using both IID (Independent and Identically Distributed) and non-IID approaches. Then, the sub-datasets are sent to these four hospitals differently for both approaches. We have used the artificial neural network as the FL base model. We trained both the IID and non-IID models and then tested the models with test data and got an accuracy of 84.87 and 86.55% for the IID and non-IID approaches, respectively. We have also checked the AUC score, sensitivity, and specificity for the IID and non-IID approaches.

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Data availability

The data that support the findings of this study are openly available at https://www.kaggle.com/datasets/sid321axn/heart-statlog-cleveland-hungary-final, [20].

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Central Institute of Technology Kokrajhar, BTR, Assam, 783370, India

    Bhabesh Mali, Santanu Saha, Daimalu Brahma, Ramakrishna Pinninti & Pranav Kumar Singh

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  1. Bhabesh Mali
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  2. Santanu Saha
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Mali, B., Saha, S., Brahma, D. et al. Towards Building a Global Robust Model for Heart Disease Detection. SN COMPUT. SCI. 4, 596 (2023). https://doi.org/10.1007/s42979-023-02083-7

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