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- W4387482421 abstract "Machine learning has the ability to predict outcomes and identify features that contribute to chronic disease prediction, enabling the classification of patients based on features. The aim of this study is to enhance the accuracy of chronic disease prediction using network analytics and machine learning, and to identify the features that contribute to this prediction. For our case study, we utilise administrative claim data, which includes patients with and without specific chronic diseases. The KNN network constructor is employed and compared to uncover hidden patient relationships. Subsequently, five network features (i.e., degree centrality, eigenvector centrality, closeness centrality, betweenness centrality, and clustering coefficient) are extracted from the network to train ensemble machine learning models along with demographic and behavioral features. The performance of various ensemble machine learning methods was analysed to determine prediction accuracy. The boosting technique exhibited exceptional accuracy for chronic disease prediction. Moreover, age, gender, and the clustering coefficient were identified as features contributing to the prediction. This research can assist healthcare practitioners in making more informed decisions from their data. Stakeholders stand to gain multiple benefits, including enhanced patient care and improved diagnostic efficiency." @default.
- W4387482421 created "2023-10-11" @default.
- W4387482421 creator A5054496165 @default.
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- W4387482421 date "2023-01-01" @default.
- W4387482421 modified "2023-10-16" @default.
- W4387482421 title "KNN-Based Patient Network and Ensemble Machine Learning for Disease Prediction" @default.
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- W4387482421 doi "https://doi.org/10.1007/978-981-99-7108-4_25" @default.
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