Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 5
September-October 2026

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“Design & Development of System for Early Detection of Thyroid Disease using Machine Learning: a Review Paper”

Author(s) Sachin K. Latad, Prof. (Dr.) S. V. Pattalwar
Country India
Abstract Thyroid problems are caused by an irregular and unnatural development of thyroid tissue at the edges about the gland that produces thyroid. Hyperthyroidism, or an overactive thyroid gland, and hypothyroidism, or
an inactive thyroid gland, are the two main types of thyroid disorders. This research suggests using effective classifiers with or without methods for feature selection by employing machine learning algorithms to identify and diagnose thyroid disease, as measured by accuracy and other performance evaluation metrics. Thyroid ultrasound image data is available on the Neural Information Processing Systems (NIPS), The Challenge of Feature Selection as well as on keggle, Github website.This research suggests three new training techniques
for multilayer perceptron (MLP) binary classifiers, two of which are based on the method of back propagation and the third is about information theory. The principle of maximal-margin (MM) serves as foundation for both back propagation techniques. The third strategy provides a strong training framework that may utilize the most effective aspects of each suggested training technique. The basic idea is to use neurons taken from three different neural networks two based on MM and the third based on LM that have all been formerly received training from Levenberg Marquard (LM), MMGDX, and MICI, respectively, to create a neural model. This study includes features like body mass index (BMI), heart rate, and blood pressure because they are directly related to thyroid diseases and are essential for obtaining the most accurate results.
Keywords ASNN(Assembled Neural Network), LevenbergMarquard(LM),Multilayer Perceptron(MLP), Maximal Margin (MM).
Published In Volume 7, Issue 5, September-October 2026
Published On 2026-09-17
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E-ISSN 3048-7641
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