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- W4384134989 abstract "Cardiac arrhythmia, an irregular heart rhythm has a high mortality rate. Early diagnosis may redeem the patient from death. Many reliable decision support systems have recently been built as “black boxes,” or systems that conceal their internal logic from the user. This absence of justification raises both ethical and practical problems. The literature describes a variety of strategies for resolving this significant flaw, often, accuracy is sacrificed for interpretability. Trust among clinical experts, particularly in medicine, can be established through the justification of decisions provided by automated medical expert systems. This work aims to integrate explainability with the Boosting model in the classification of cardiac arrhythmia diagnosis. The reasons behind the model’s output are explained using the familiar explainable AI tool LIME in a simple and comprehensible manner." @default.
- W4384134989 created "2023-07-14" @default.
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- W4384134989 date "2023-01-01" @default.
- W4384134989 modified "2023-09-24" @default.
- W4384134989 title "XACML: Explainable Arrhythmia Classification Model Using Machine Learning" @default.
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- W4384134989 doi "https://doi.org/10.1007/978-3-031-35641-4_18" @default.
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