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The need for enhanced methods in disease prediction is a significant challenge in the medical field. Current predictive models often face challenges such as limited accuracy, insufficient adaptability to diverse datasets, and inefficiencies in feature selection and model training. These limitations can hinder early diagnosis and effective management of thyroid conditions, which are vital for patient outcomes. The study introduces an innovative method for enhancing thyroid disease prediction using a machine learning study employs algorithms such as support vector machine (SVM), Naive Bayes (NB), K-nearest neighbor (KNN), logistic regression (LR), and stochastic gradient descent (SGD) in conjunction with filter, wrapper, and embedded feature selection methods across three distinct models. The study uses two thyroid datasets, one from Dew Medicare Ternity Hospital, Nagpur, and the other from the UCI thyroid repository, revealing the potential of the novel 'FeatureBoostThyro' approach for improving thyroid risk prediction across diverse datasets. The proposed method achieved accuracies of 98.10%, 97.47%, and 95.58% for the three models using the UCI dataset, and 97.42%, 98.71%, and 97.83% for the DMTH dataset. The novelty of this approach lies in its integrated pipeline that ensures the selection of the best features, systematic model training, and rigorous evaluation. This results in a robust, accurate, and reliable model that outperforms traditional approaches, making it a significant advancement in the field of disease prediction. The enhanced performance metrics, especially accuracy, highlight the potential of this method in clinical settings for early and accurate thyroid disease detection. [ABSTRACT FROM AUTHOR]

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 model selection 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. [ABSTRACT FROM AUTHOR]