Thyroid Disease Prediction Using XGBoost Algorithms

Sankar, Sharmila; Potti, Anupama; Chandrika, G. Naga; Ramasubbareddy, Somula

doi:10.13052/jmm1550-4646.18322

Cited by 25 publications

(15 citation statements)

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“…We deployed study [ 19 ] which used RF for the thyroid disease prediction. Similarly, we deployed study, [ 21 ] which used DT for thyroid disease prediction. Another study [ 20 ] is used, which worked on thyroid disease and proposed DNN.…”

Section: Resultsmentioning

confidence: 99%

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Chaganti

Rustam

Díez

et al. 2022

Cancers

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Thyroid disease prediction has emerged as an important task recently. Despite existing approaches for its diagnosis, often the target is binary classification, the used datasets are small-sized and results are not validated either. Predominantly, existing approaches focus on model optimization and the feature engineering part is less investigated. To overcome these limitations, this study presents an approach that investigates feature engineering for machine learning and deep learning models. Forward feature selection, backward feature elimination, bidirectional feature elimination, and machine learning-based feature selection using extra tree classifiers are adopted. The proposed approach can predict Hashimoto’s thyroiditis (primary hypothyroid), binding protein (increased binding protein), autoimmune thyroiditis (compensated hypothyroid), and non-thyroidal syndrome (NTIS) (concurrent non-thyroidal illness). Extensive experiments show that the extra tree classifier-based selected feature yields the best results with 0.99 accuracy and an F1 score when used with the random forest classifier. Results suggest that the machine learning models are a better choice for thyroid disease detection regarding the provided accuracy and the computational complexity. K-fold cross-validation and performance comparison with existing studies corroborate the superior performance of the proposed approach.

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Section: Resultsmentioning

confidence: 99%

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Chaganti

Rustam

Díez

et al. 2022

Cancers

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“…However, in our research, balancing the dataset and employing SSC for classification contributed to the maximum performance. We also deployed the designed approaches cited in [ 19 , 28 , 48 , 49 ] on our dataset to signify the performance of the proposed approach. Table 14 provides a detailed comparison of the results that illustrates the significance of the proposed SSC model.…”

Section: Comparative Analysis Of Experimental Resultsmentioning

confidence: 99%

SSC: The novel self-stack ensemble model for thyroid disease prediction

2024

PLoS ONE

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Thyroid disease presents a significant health risk, lowering the quality of life and increasing treatment costs. The diagnosis of thyroid disease can be challenging, especially for inexperienced practitioners. Machine learning has been established as one of the methods for disease diagnosis based on previous studies. This research introduces a novel and more effective technique for predicting thyroid disease by utilizing machine learning methodologies, surpassing the performance of previous studies in this field. This study utilizes the UCI thyroid disease dataset, which consists of 9172 samples and 30 features, and exhibits a highly imbalanced target class distribution. However, machine learning algorithms trained on imbalanced thyroid disease data face challenges in reliably detecting minority data and disease. To address this issue, re-sampling is employed, which modifies the ratio between target classes to balance the data. In this study, the down-sampling approach is utilized to achieve a balanced distribution of target classes. A novel RF-based self-stacking classifier is presented in this research for efficient thyroid disease detection. The proposed approach demonstrates the ability to diagnose primary hypothyroidism, increased binding protein, compensated hypothyroidism, and concurrent non-thyroidal illness with an accuracy of 99.5%. The recommended model exhibits state-of-the-art performance, achieving 100% macro precision, 100% macro recall, and 100% macro F1-score. A thorough comparative assessment is conducted to demonstrate the viability of the proposed approach, including several machine learning classifiers, deep neural networks, and ensemble voting classifiers. The results of K-fold cross-validation provide further support for the efficacy of the proposed self-stacking classifier.

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“…Each time a tree is added, a new function is learned, and then the residual error of the last prediction is fitted. Finally, according to the tree's structure, the optimal score under this structure can be obtained, and the total score can be calculated through the leaf nodes of each tree [25,26].…”

Section: Methodsmentioning

confidence: 99%

Fraud detection under the unbalanced class based on gradient boosting

Alothman

Talib

Mohammed

2022

EEJET

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Credit fraud modeling is an important topic covered by researchers. Overdue risk management is a critical business link in providing credit loan services. It directly impacts the rate of return and the bad debt percentage of lending organizations in this sector. Credit financial services have benefited the general public as a result of the development of the mobile Internet, and overdue risk control has evolved from the manual judgment that relied on rules in the past to a credit model built using a large amount of customer data to predict the likelihood of customers becoming delinquent. When creating a credit rating model, the emerging nature of the credit samples makes the minority class sample score very few; that is, when a large number of actual samples are obtained, this causes machine learning models to be biased towards the majority class when training. Traditional data balancing methods can reduce the bias of models to the majority category when the data is relatively unbalanced rather than excessive. Gradient boosting algorithms (XGBoost and CatBoost) are proposed in this paper to model highly unbalanced data to detect credit fraud. To find hyperparameters and determine the accuracy of the minority class as an optimization function of the model, Bayesian optimization is used to increase the model's accuracy for the minority class. The paper was tested with real European credit card fraud data. The results were compared to traditional machine learning (decision trees and logistic regression) and the performance of the bagging algorithm (random forest). For comparison, the traditional data balancing method (Oversample) is used

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Thyroid Disease Prediction Using XGBoost Algorithms

Cited by 25 publications

References 0 publications

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

SSC: The novel self-stack ensemble model for thyroid disease prediction

Fraud detection under the unbalanced class based on gradient boosting

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