A data-driven principal component analysis-support vector machine approach for breast cancer diagnosis: Comparison and application

Wu, Wen; Faisal, Shah

doi:10.1177/0142331219889221

Cited by 8 publications

(4 citation statements)

References 37 publications

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“…, where r and g denote kernel parameter. 25 The kernel is also known as the radial basis function (RBF). � � insensitive loss function is used in the SVR.…”

Section: Decision Tree Regression (Dtr)mentioning

confidence: 99%

The design and development of a diesel engine electromechanical EGR cooling system based on machine learning-genetic algorithm prediction models to reduce emission and fuel consumption

Kaleli

Akolaş

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Data-driven modelling techniques have recently been used in the development of engine design and control systems for defining the engine in-cylinder complex combustion process. The aim of this investigation is to improve exhaust emissions and fuel consumption by designing an electromechanical exhaust gas recirculation (EGR) cooling system consisting of an electric water pump and fan unlike conventional systems. To determine the effects of the EGR ratio and the temperature of the exhaust gas entering the intake manifold on the diesel engine parameters of nitrogen oxides (NOx) emission and brake specific fuel consumption (BSFC), four learning (ML) algorithms were adapted according to statistical evaluation criteria such as root mean squared error (RMSE), coefficient of determination (R2), mean squared error (MSE) and mean absolute error (MAE). The hyper parameters of the selected best model among four learning algorithms were determined by using grid search method. The results showed that the Gaussian process regression model (GPR) outperformed other ML models according to success error prediction of NOx and BSFC. Then, performance of the designed electromechanical EGR cooling system was analyzed under global driving conditions, the New European driving cycle (NEDC), and the world-wide harmonized light duty test procedure (WLTP). In these test cycles, global optimization was utilized with the GPR model as the objective function based on minimizing NOx and BSFC. Consequently, this study demonstrates the potential of the proposed system based on ML-GA to reduce NOx and BSFC by achieving reductions of 13.7%(NEDC)–9.98%(WLTP) and 2.61%(NEDC)–2.07%(WLTP) in NEDC and WLTP conditions, respectively compared to the conventional EGR cooling approach.

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“…, where r and g denote kernel parameter. 25 The kernel is also known as the radial basis function (RBF). � � insensitive loss function is used in the SVR.…”

Section: Decision Tree Regression (Dtr)mentioning

confidence: 99%

The design and development of a diesel engine electromechanical EGR cooling system based on machine learning-genetic algorithm prediction models to reduce emission and fuel consumption

Kaleli

Akolaş

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Developing a model for breast cancer diagnosis with machine learning has been effective. For instance, several studies [16]- [19] have suggested that the use of a machine learning model for breast cancer diagnosis is encouraging. The machine-learning model achieved 97.77%.…”

Section: Introductionmentioning

confidence: 99%

Explainable extreme boosting model for breast cancer diagnosis

Suresh

Assegie²,

Ganesan³

et al. 2023

IJECE

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<span lang="EN-US">This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains the XGB model diagnosis outcome showing the features affecting the XGBoost model. The developed XGB model achieves an accuracy of 98.42%.</span>

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“…By establishing a relationship between key and process variables within the available data, data-driven models can be used to predict critical product quality in the process industry (Chen et al, 2022). Data-driven models, such as latent variable models (LVMs) (including principal component analysis (PCA) (Liang et al, 2021) and partial least squares (PLS) (Chiplunkar and Huang, 2019), artificial neural networks (ANNs) (Kazmi et al, 2023), generative adversarial network (GAN) (Shao et al, 2023), Gaussian process regression (GPR) (Deringer et al, 2021) and support vector machines (SVMs) (Wu and Faisal, 2020)), have been widely used in various process controls. Although these methods are effective, they often treat measurement data as discrete samples and analyse them using multivariate statistical methods, which can overlook the continuous characteristics of process variables.…”

Section: Introductionmentioning

confidence: 99%

A data-driven soft-sensing approach using probabilistic latent variable model with functional data framework

Tan

Guo

Liu

et al. 2023

Transactions of the Institute of Measurement and Control

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Functional principal component analysis (FPCA) and functional partial least squares (FPLS) are two mainstream functional data analysis (FDA) methods, which have been commonly used to extract deep information hidden in the original data space. However, the process data always contain random noise, which affects the performance of FDA models. To overcome this issue, two functional probabilistic latent variable models (FPLVMs), including functional probabilistic principal component analysis (FPPCA) and functional probabilistic partial least squares (FPPLS) are proposed in this work. First, the process data are converted into functional data using the FDA. Subsequently, a log-likelihood function considering the noise factor and functional latent variables is designed. Finally, the regression model parameters are estimated using an expectation–maximisation algorithm. In contrast to FPPCA, FPPLS decomposes the process data and the key variable with constrained latent variables, which is similar to the partial least squares (PLS) and the principal component analysis (PCA). Moreover, the degeneration mechanism from FPLVMs into probabilistic latent variable models and latent variable models is discussed. An adaptive strategy with functional covariance is used to satisfy the online predictive capabilities of the model. Finally, the proposed approach is validated using a numerical case, the Tennessee Eastman process and an industrial o-xylene distillation column for evaluation.

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A data-driven principal component analysis-support vector machine approach for breast cancer diagnosis: Comparison and application

Cited by 8 publications

References 37 publications

The design and development of a diesel engine electromechanical EGR cooling system based on machine learning-genetic algorithm prediction models to reduce emission and fuel consumption

The design and development of a diesel engine electromechanical EGR cooling system based on machine learning-genetic algorithm prediction models to reduce emission and fuel consumption

Explainable extreme boosting model for breast cancer diagnosis

A data-driven soft-sensing approach using probabilistic latent variable model with functional data framework

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