Key result
XcepFRSNet deep learning model detects heart disease with ~91% accuracy.
Why the study?
Does the XcepFRSNet deep learning model accurately detect heart disease?
Does the XcepFRSNet deep learning model accurately detect heart disease?
The XcepFRSNet hybrid deep learning model demonstrates high accuracy and sensitivity for automated heart disease detection, potentially aiding in prompt diagnosis.
In the current advanced universe, Heart Disease (HD) is considered the most dangerous. Since this disease affects a person very quickly, they have little time to receive treatment. Therefore, accurately and promptly examining patients is a major challenge for medical institutions. A poor examination by the hospital can lead to negative opinions and damage its reputation. At the same time, the cost of treatment becomes high, making it unaffordable for many patients. To improve heart disease detection, a hybrid deep learning model, Xception Fused Residual SqueezeNet (XcepFRSNet), is developed. From the chosen database, the input data is standardized through [Formula: see text]-score normalization, and missing entries are imputed to prepare it for subsequent processing. The Wave-Hedges metric is employed for feature selection, and bootstrapping is subsequently applied to augment the data. The proposed XcepFRSNet model performs heart disease prediction, incorporating layer modifications guided by the Taylor concept. The proposed XcepFRSNet demonstrates strong results, attaining an accuracy of 90.942%, a sensitivity of 91.345%, and specificity of 89.67%.
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Padhy et al. (2026) studied Heart Disease. XcepFRSNet (Xception Fused Residual SqueezeNet) was evaluated on Heart disease prediction. The proposed XcepFRSNet deep learning model for heart disease detection achieved an accuracy of 90.942%, a sensitivity of 91.345%, and a specificity of 89.67%.
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