Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data

Khodabandelu, Sajad; Ghaemian, Naser; Khafri, Soraya; Ezoji, Mehdi; Khaleghi, Sara

doi:10.34172/jrhs.2022.90

Cited by 3 publications

(3 citation statements)

References 34 publications

(36 reference statements)

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“…All SPN patients were randomly divided into the training and test sets by 7꞉3 and the patients in the training set were balanced at a ratio of 1:1 by using the synthetic minority oversampling technique, so that the number of benign SPN patients and malignant SPN patients was consistent 35 , 36 . The LASSO algorithm was used to screen features from patients’ clinical data and DLCT parameters, and 6 classical ML models were constructed in the training set based on the selected features, namely AdaBoost, GNB, LR, RF, SVM and XGBoost 37 .…”

Section: Methodsmentioning

confidence: 99%

Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules

Lu,

Liu,

Zhao

et al. 2024

Sci Rep

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The benign and malignant status of solitary pulmonary nodules (SPNs) is a key determinant of treatment decisions. The main objective of this study was to validate the efficacy of machine learning (ML) models featured with dual-layer detector spectral computed tomography (DLCT) parameters in identifying the benign and malignant status of SPNs. 250 patients with pathologically confirmed SPN were included in this study. 8 quantitative and 16 derived parameters were obtained based on the regions of interest of the lesions on the patients’ DLCT chest enhancement images. 6 ML models were constructed from 10 parameters selected after combining the patients' clinical parameters, including gender, age, and smoking history. The logistic regression model showed the best diagnostic performance with an area under the receiver operating characteristic curve (AUC) of 0.812, accuracy of 0.813, sensitivity of 0.750 and specificity of 0.791 on the test set. The results suggest that the ML models based on DLCT parameters are superior to the traditional CT parameter models in identifying the benign and malignant nature of SPNs, and have greater potential for application.

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Section: Methodsmentioning

confidence: 99%

Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules

Lu,

Liu,

Zhao

et al. 2024

Sci Rep

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“…However, imbalanced data are commonly observed in electronic nose studies, but methods to address imbalanced data have seldom been reported [ 35 ]. Imbalanced data can negatively affect the accuracy of a diagnostic test by leading to biased models that perform poorly on minority classes [ 9 ]. When a diagnostic test is trained on an imbalanced dataset, the model may overfit to the majority class and underfit to the minority class, leading to better performance on the former.…”

Section: Introductionmentioning

confidence: 99%

Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data

Chen,

Kuo,

Shie

et al. 2024

Respir Res

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Background Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported. Objective The objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm. Methods We conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard. Results We enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84–0.91), the summary specificity was 1.00 (95% CI 0.85–1.00), the AUC was 0.96 (95% CI 0.94–0.98), the pAUC was 0.92 (95% CI 0.89–0.96), and the DOR was 207.62 (95% CI 24.62–924.64). Conclusion Electronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses.

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“…If the assumptions are violated, biased estimations and improper inferences can be obtained. 8 Machine learning techniques as applied statistical methods have been considerably utilized in data analysis. These techniques do not contain pre-defined relationships between study variables, and the prediction is available without needing to understand essential mechanisms.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas

et al. 2023

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Background: Determining suburban area crashes’ risk factors may allow for early and operative safety measures to find the main risk factors and moderating effects of crashes. Therefore, this paper has focused on a causal modeling framework. Study Design: A cross-sectional study. Methods: In this study, 52524 suburban crashes were investigated from 2015 to 2016. The hybrid-random-forest-generalized-path-analysis technique (HRF-gPath) was used to extract the main variables and identify mediators and moderators. Results: This study analyzed 42 explanatory variables using a RF model, and it was found that collision type, distinct, driver misconduct, speed, license, prior cause, plaque description, vehicle maneuver, vehicle type, lighting, passenger presence, seatbelt use, and land use were significant factors. Further analysis using g-Path demonstrated the mediating and predicting roles of collision type, vehicle type, seatbelt use, and driver misconduct. The modified model fitted the data well, with statistical significance ( χ230 =81.29, P<0.001) and high values for comparative-fit-index and Tucker-Lewis-index exceeding 0.9, as well as a low root-mean-square-error-of-approximation of 0.031 (90% confidence interval: 0.030-0.032). Conclusion: The results of our study identified several significant variables, including collision type, vehicle type, seatbelt use, and driver misconduct, which played mediating and predicting roles. These findings provide valuable insights into the complex factors that contribute to collisions via a theoretical framework and can inform efforts to reduce their occurrence in the future.

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Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data

Cited by 3 publications

References 34 publications

Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules

Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules

Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data

A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas

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