Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

Islam, Saima Sharleen; Haque, Masudul; Miah, M. Saef Ullah; Sarwar, Talha Bin; Nugraha, Ramdhan

doi:10.7717/peerj-cs.898

Cited by 58 publications

(70 citation statements)

References 35 publications

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“…Their contributions might be masked by factorial interactions in statistical approaches. Similar phenomena were reported for thyroid disease, chronic kidney disease, and analgesic adverse-effect prediction [ 54 , 62 , 63 ]. The machine learning models identified relevant features with nonlinear relationships and complex interactions between factors and outcomes, such as HDL and LDL, in the present study; Promising to help doctors and pharmacists to pay special attention when checking amiodarone users’ lipid panels to prevent adverse thyroid events.…”

Section: Discussionsupporting

confidence: 82%

Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation

Chao

Chiang

et al. 2023

J Med Internet Res

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Background Machine learning offers new solutions for predicting life-threatening, unpredictable amiodarone-induced thyroid dysfunction. Traditional regression approaches for adverse-effect prediction without time-series consideration of features have yielded suboptimal predictions. Machine learning algorithms with multiple data sets at different time points may generate better performance in predicting adverse effects. Objective We aimed to develop and validate machine learning models for forecasting individualized amiodarone-induced thyroid dysfunction risk and to optimize a machine learning–based risk stratification scheme with a resampling method and readjustment of the clinically derived decision thresholds. Methods This study developed machine learning models using multicenter, delinked electronic health records. It included patients receiving amiodarone from January 2013 to December 2017. The training set was composed of data from Taipei Medical University Hospital and Wan Fang Hospital, while data from Taipei Medical University Shuang Ho Hospital were used as the external test set. The study collected stationary features at baseline and dynamic features at the first, second, third, sixth, ninth, 12th, 15th, 18th, and 21st months after amiodarone initiation. We used 16 machine learning models, including extreme gradient boosting, adaptive boosting, k-nearest neighbor, and logistic regression models, along with an original resampling method and 3 other resampling methods, including oversampling with the borderline-synthesized minority oversampling technique, undersampling–edited nearest neighbor, and over- and undersampling hybrid methods. The model performance was compared based on accuracy; Precision, recall, F1-score, geometric mean, area under the curve of the receiver operating characteristic curve (AUROC), and the area under the precision-recall curve (AUPRC). Feature importance was determined by the best model. The decision threshold was readjusted to identify the best cutoff value and a Kaplan-Meier survival analysis was performed. Results The training set contained 4075 patients from Taipei Medical University Hospital and Wan Fang Hospital, of whom 583 (14.3%) developed amiodarone-induced thyroid dysfunction, while the external test set included 2422 patients from Taipei Medical University Shuang Ho Hospital, of whom 275 (11.4%) developed amiodarone-induced thyroid dysfunction. The extreme gradient boosting oversampling machine learning model demonstrated the best predictive outcomes among all 16 models. The accuracy; Precision, recall, F1-score, G-mean, AUPRC, and AUROC were 0.923, 0.632, 0.756, 0.688, 0.845, 0.751, and 0.934, respectively. After readjusting the cutoff, the best value was 0.627, and the F1-score reached 0.699. The best threshold was able to classify 286 of 2422 patients (11.8%) as high-risk subjects, among which 275 were true-positive patients in the testing set. A shorter treatment duration; higher levels of thyroid-stimulating hormone and high-density lipoprotein cholesterol; and lower levels of free thyroxin, alkaline phosphatase, and low-density lipoprotein were the most important features. Conclusions Machine learning models combined with resampling methods can predict amiodarone-induced thyroid dysfunction and serve as a support tool for individualized risk prediction and clinical decision support.

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Section: Discussionsupporting

confidence: 82%

Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation

Chao

Chiang

et al. 2023

J Med Internet Res

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“…In this complexity, atypical or milder cases may remain elusive due to the rigid structures of diagnostic tools, leading to potential delays in diagnosis and intervention. [3] Like regional accents in a theatrical performance, regional differences add another layer to the thyroid storm saga. [3] Different regions and institutions adopting distinct criteria or scoring systems introduce variations in the diagnosis and management of thyroid storm.…”

Section: Emergency Medicine Guidelinesmentioning

confidence: 99%

Diagnostic criteria and scoring systems for thyroid storm: An evaluation of their utility – comparative review

Elendu,

Amaechi,

Amaechi

et al. 2024

Medicine

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A thyroid storm is a life-threatening endocrine emergency characterized by severe hyperthyroidism and many systemic manifestations. Prompt recognition and treatment are essential for patient survival. This study evaluates the utility of existing diagnostic criteria and scoring systems for thyroid storm. A comprehensive literature review encompassed articles published up to December 2023. Various diagnostic criteria and scoring systems, such as the Burch–Wartofsky Point Scale and the Japanese Thyroid Association criteria, were critically assessed based on their sensitivity, specificity, and clinical applicability. Our findings reveal that existing diagnostic criteria and scoring systems, although valuable tools, exhibit limitations. They may lack sensitivity in identifying milder cases of thyroid storm or fail to differentiate it from other critical conditions. Furthermore, some criteria rely heavily on subjective clinical Judgment, which can vary among healthcare providers. Future research should focus on refining existing criteria and developing more objective and universally applicable diagnostic tools to address these limitations. Incorporating advanced laboratory markers and modern imaging techniques may enhance diagnostic accuracy. Additionally, a standardized scoring system approach could improve clinical practice consistency. In conclusion, while current diagnostic criteria and scoring systems provide a foundation for identifying thyroid storm, their utility has shortcomings. Advancements in diagnostic methods and a collaborative effort to establish standardized criteria are imperative to enhance the accuracy and reliability of thyroid storm diagnosis, ultimately improving patient outcomes.

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“…22 Additionally, rather than eliminating several duplicates, SMOTE methods generate new unique data. 22,24 Since SMOTE reduces the probability of overfitting, 18 this study uses SMOTE to balance the datasets.…”

Section: Synthetic Minority Oversampling Technique (Smote)mentioning

confidence: 99%

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT

Thakur

Kankar

Parey

et al. 2023

Proc Inst Mech Eng H

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This work provides an innovative endodontic instrument fault detection methodology during root canal treatment (RCT). Sometimes, an endodontic instrument is prone to fracture from the tip, for causes uncertain the dentist’s control. A comprehensive assessment and decision support system for an endodontist may avoid several breakages. This research proposes a machine learning and artificial intelligence-based approach that can help to diagnose instrument health. During the RCT, force signals are recorded using a dynamometer. From the acquired signals, statistical features are extracted. Because there are fewer instances of the minority class (i.e. faulty/moderate class), oversampling of datasets is required to avoid bias and overfitting. Therefore, the synthetic minority oversampling technique (SMOTE) is employed to increase the minority class. Further, evaluating the performance using the machine learning techniques, namely Gaussian Naïve Bayes (GNB), quadratic support vector machine (QSVM), fine k-nearest neighbor (FKNN), and ensemble bagged tree (EBT). The EBT model provides excellent performance relative to the GNB, QSVM, and FKNN. Machine learning (ML) algorithms can accurately detect endodontic instruments’ faults by monitoring the force signals. The EBT and FKNN classifier is trained exceptionally well with an area under curve values of 1.0 and 0.99 and prediction accuracy of 98.95 and 97.56%, respectively. ML can potentially enhance clinical outcomes, boost learning, decrease process malfunctions, increase treatment efficacy, and enhance instrument performance, contributing to superior RCT processes. This work uses ML methodologies for fault detection of endodontic instruments, providing practitioners with an adequate decision support system.

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Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

Cited by 58 publications

References 35 publications

Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation

Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation

Diagnostic criteria and scoring systems for thyroid storm: An evaluation of their utility – comparative review

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT

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