A Robust Personalized Classification Method for Breast Cancer Metastasis Prediction

Adnan, Nahim; Najnin, Tanzira; Ruan, Jianhua

doi:10.3390/cancers14215327

Cited by 4 publications

(2 citation statements)

References 34 publications

(38 reference statements)

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“…Second, setting the number of species of a classifier too large greatly reduces the accuracy of the predictions obtained from the model in the presence of noise and insufficiently distinctive fault characteristics [7].…”

Section: Data Design and Generation For Convolutional Neural Network ...mentioning

confidence: 99%

Card fault diagnosis of the pressurized water reactor off-heap nuclear measurement system based on expert experience and convolutional neural network

Jin,

Lu,

Guan

et al. 2024

J. Inst.

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The reactor nuclear measurement system is important in a nuclear power plant. Its main role is to measure the reactor's core power distribution using detectors and calibrate and provide data on the core fuel consumption. This study describes the lack of fault data and the lack of diagnostic methodology research in the overhauling process and fault diagnosis of the off-heap nuclear measurement system core card. This core card provides the detectors with the necessary working conditions. It also collects signals. In this study, we propose a methodology for the fault diagnosis of the card through circuit analysis, simulation of functional module division, fault data generation, and training of a convolutional neural network diagnostic model. The proposed methodology can transform the drawings into convenient diagnostic processes and algorithms based on expert experience. These drawings are difficult to use in actual overhauling conditions. The corresponding experimental equipment was designed for practical testing. The experimental results show that the accuracy of the obtained diagnostic model for classifying preset faults can reach 99.5%, indicating that this model can be applied in actual working conditions. The accuracy of the trained diagnostic model in classifying 13 kinds of faults in the training set during the actual test was tested. Results show that the accuracy rate is close to 100%. Moreover, the correction of the model using the real maintenance data in applying the actual maintenance conditions was also analyzed. The intelligent diagnostic system that centers on the fault diagnosis method investigated in this study has been applied in the pressurized water reactor off-heap nuclear measurement system digital transformation and upgrading project of Qinshan No. 2 Plant.

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Section: Data Design and Generation For Convolutional Neural Network ...mentioning

confidence: 99%

Card fault diagnosis of the pressurized water reactor off-heap nuclear measurement system based on expert experience and convolutional neural network

Jin,

Lu,

Guan

et al. 2024

J. Inst.

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show abstract

“…Synthetic dataset 1 represents the most ideal scenario, characterized by two additional features exhibiting predictive power over different regions of the initial features value space, as shown in Figure 3. Conversely, synthetic dataset 2 illuminates the necessity for sample-weighted inference (as indicated by LW) when confronted with non-linear predictive regions highlighted in Figure 4 24,25 . Furthermore, synthetic dataset 3 serves as a testament to the robustness of our framework, particularly in scenarios involving overlapping regions on the initial features value space that can be seen in Figure 5.…”

Section: Reasoning Process Of the Frameworkmentioning

confidence: 99%

Individualized Machine-learning-based Clinical Assessment Recommendation System

Setiawan,

Wiranto,

Girard

et al. 2024

Preprint

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Background: Traditional clinical assessments often lack individualization, relying on standardized procedures that may not accommodate the diverse needs of patients, especially in early stages where personalized diagnosis could offer significant benefits. We aim to provide a machine-learning framework that addresses the individualized feature addition problem and enhances diagnostic accuracy for clinical assessments. Methods: Individualized Clinical Assessment Recommendation System (iCARE) employs locally weighted logistic regression and Shapley Additive Explanations (SHAP) value analysis to tailor feature selection to individual patient characteristics. Evaluations were conducted on synthetic and real-world datasets, including early-stage diabetes risk prediction and heart failure clinical records from the UCI Machine Learning Repository. We compared the performance of iCARE with a Global approach using statistical analysis on accuracy and area under the ROC curve (AUC) to select the best additional features. Findings: The iCARE framework enhances predictive accuracy and AUC metrics when additional features exhibit distinct predictive capabilities, as evidenced by synthetic datasets 1-3 and the early diabetes dataset. Specifically, in synthetic dataset 1, iCARE achieved an accuracy of 0.999 and an AUC of 1.000, outperforming the Global approach with an accuracy of 0.689 and an AUC of 0.639. In the early diabetes dataset, iCARE shows improvements of 1.5-3.5% in accuracy and AUC across different numbers of initial features. Conversely, in synthetic datasets 4-5 and the heart failure dataset, where features lack discernible predictive distinctions, iCARE shows no significant advantage over global approaches on accuracy and AUC metrics. Interpretation: iCARE provides personalized feature recommendations that enhance diagnostic accuracy in scenarios where individualized approaches are critical, improving the precision and effectiveness of medical diagnoses. Funding: This work was supported by startup funding from the Department of Psychology at the University of Kansas provided to A.A., and the R01MH125740 award from NIH partially supported J.M.G.'s work.

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A Light Gradient-Boosting Machine algorithm with Tree-Structured Parzen Estimator for breast cancer diagnosis

Omotehinwa¹,

Oyewola

Dada

2023

Healthcare Analytics

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A Robust Personalized Classification Method for Breast Cancer Metastasis Prediction

Cited by 4 publications

References 34 publications

Card fault diagnosis of the pressurized water reactor off-heap nuclear measurement system based on expert experience and convolutional neural network

Card fault diagnosis of the pressurized water reactor off-heap nuclear measurement system based on expert experience and convolutional neural network

Individualized Machine-learning-based Clinical Assessment Recommendation System

A Light Gradient-Boosting Machine algorithm with Tree-Structured Parzen Estimator for breast cancer diagnosis

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