Embedded Feature Selection Augmented Thyroid Disorder Prediction using MLP

Embedded Feature Selection Augmented Thyroid Disorder Prediction using MLP

Thyroid Disorder, Feature Selection, Classification, Deep Neural Network.

Due to its considerable fatality rate and increasing frequency, thyroid disorders pose a severe hazard to people's health in the modern era. Thus, it has become a useful topic to predict thyroid disease early on using a few basic physical indications that are gathered from routine physical examinations. Being aware of these thyroid-related signs is crucial from a clinical standpoint in order to forecast outcomes and offer a solid foundation for additional diagnosis. However, manual analysis and prediction are difficult and tiring due to the vast volume of data. Our goal is to use a variety of bodily signs to swiftly and reliably predict thyroid disorders. This research presents a novel prediction model for thyroid disorders. We provide a deep neural network and embedded feature selection method-based algorithm for predicting thyroid disorders. Based on the LinearSVC algorithm, this embedded feature selection method selects a subset of characteristics that are strongly linked

Mir Saleem1, Shabir Najar2, Malik Akhtar Rasool3

www.ijcit.com

December 2024

peri irawan

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