Enhancing Classification Performance through FeatureBoostThyro - A Comparative Study of Machine Learning Algorithms and Feature Selection

Aug 3, 2026

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Early-stage disease prediction is a challenging task, and machine learning techniques are used to improve diagnostic reliability. Thyroid disorders affect about 42 million people in India and require effective classification methods. This paper introduces the FeatureBoostThyro (FBT) model to enhance classification performance by combining multiple machine learning algorithms with diverse feature selection techniques. Algorithms including SGD, KNN, LR, NB, and SVM are evaluated using information gain, relief F, chi-square, gini index, forward/backward selection, RFE, and LASSO. Results compare accuracy, precision, recall, and F1-score on a hospital dataset and the UCI dataset.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

Introduction

Literature Survey

Methodology and Algorithms

Feature Selection Techniques

Classification Algorithms

Experimental Setup and Datasets

Results and Discussion

Conclusion

JOE International Journal of Online and Biomedical Engineering [Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org) iJOE | eISSN: 2626-8493 | Vol. 20 No. 4 (2024) | [https://doi.org/10.3991/ijoe.v20i04.45413](https://doi.org/10.3991/ijoe.v20i04.45413) PAPER Enhancing Classification Performance through FeatureBoostThyro: A Comparative Study of Machine Learning Algorithms and Feature Selection Deepali Bhende1(*), Gopal Sakarkar1,2, Punam Khandar3, Satyajit Uparkar3, Arvind Bhave4 1G. H. Raisoni University, Saikheda, Madhya Pradesh, India 2Dr. Vishwanath Karad, MIT World Peace University, Pune, Maharashtra, India 3Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India 4Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, Maharashtra, India deepali.bhende.phdcs@ [ghru.edu.in](ghru.edu.in) ABSTRACT Early-stage prediction of a disease is an important and challenging task. The application of machine learning techniques is playing an important role in this era. Thyroid is one of the chronic endocrine diseases, and approximately 42 million people in India are affected by this disease. This paper presents a comprehensive investigation into the enhancement of classification performance through the novel ‘FeatureBoostThyro’(FBT) model. The study evaluates various machine learning algorithms, including stochastic gradient descent (SGD), K nearest neighbor (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM), in conjunction with diverse feature selection methods. The research systematically explores the impact of feature selection techniques such as information gain, relief F, chi-square, gini index, forward selection, backward selection, recursive feature elimination, and LASSO on model performance across the chosen algorithms. The analysis reveals notable variations in performance metrics, including accuracy, precision, recall, and F1-score, providing valuable insights into the interplay between algorithm and feature selection. One main contribution of this research is the introduction of the FBT model, which consistently outperforms other models across various feature selection methods, making it a promising tool for addressing complex classification tasks. The findings contribute to a broader understanding of modelselection and optimization in machine learning applications. The proposed model undergoes evaluation using two distinct datasets: the primary dataset acquired from Lata Mangeshkar Hospital in Nagpur and the secondary dataset obtained from the UCI dataset. KEYWORDS machine learning, thyroid disorder, feature selection, logistic regression, gradient descent, information gain, recursive feature elimination Bhende, D., Sakarkar, G., Khandar, P., Uparkar, S., Bhave, A. (2024) . Enhancing Classification Performance through FeatureBoostThyro: A Comparative Study of Machine Learning Algorithms and Feature Selection. InternationalJournal of Online and Biomedical Engineering (iJOE), 20(4), pp. 29–42. [https://doi](https://doi). org/10.3991/ijoe.v20i04.45413 Article submitted 2023-09-30. Revision uploaded 2023-12-11. Final acceptance 2023-12-11. © 2024 by the authors of this article. Published under CC-BY. iJOE | Vol. 20 No. 4 (2024) International Journal of Online and Biomedical Engineering (iJOE) 29 Bhende et al. 1 INTRODUCTION There are several chronic diseases, and treating these chronic diseases is a difficult challenge for doctors [1] . One of the chronic disorders is thyroid disease. The thyroid is located on the front side of the neck, and its malfunction results in this disease. The trachea is surrounded by endocrine gland. It has the shape of a butterfly. The thyroid gland produces the hormone that regulates many important bodily functions. Thyroid illness occurs when the thyroid gland fails to produce the appropriate amount of thyroid hormones. These hormones’ f

FAQs

What is the main goal of the FeatureBoostThyro (FBT) study?

To enhance classification performance for early-stage thyroid disorder prediction by combining machine learning algorithms with multiple feature selection methods. The study also evaluates how algorithm choice interacts with feature selection impact.

Which machine learning algorithms are evaluated in the paper?

The paper evaluates SGD, KNN, logistic regression (LR), naive Bayes (NB), and support vector machine (SVM). Each is tested alongside several feature selection strategies.

How are feature selection techniques assessed and which metrics are used for comparison?

Feature selection techniques such as information gain, relief F, chi-square, gini index, forward selection, backward selection, recursive feature elimination, and LASSO are applied systematically. Model performance is compared using accuracy, precision, recall, and F1-score.

Aug 3, 2026

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