Embedded Feature Selection Augmented Thyroid Disorder Prediction using MLP
Dublin Core
Title
Embedded Feature Selection Augmented Thyroid Disorder Prediction using MLP
Subject
Thyroid Disorder, Feature Selection, Classification, Deep Neural Network.
Description
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
Creator
Mir Saleem1, Shabir Najar2, Malik Akhtar Rasool3
Source
www.ijcit.com
Date
December 2024
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Mir Saleem1, Shabir Najar2, Malik Akhtar Rasool3, “Embedded Feature Selection Augmented Thyroid Disorder Prediction using MLP,” Repository Horizon University Indonesia, accessed June 5, 2025, https://repository.horizon.ac.id/items/show/9176.