Comparison of Logistic Regression and Bayesian Networks for Risk Prediction of Breast Cancer Recurrence

Witteveen, Annemieke; Nane, Gabriela F.; Vliegen, Ingrid; Siesling, Sabine; IJzerman, Maarten J.

doi:10.1177/0272989x18790963

Cited by 37 publications

(27 citation statements)

References 50 publications

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“…Our results suggest that BNs can perform on par with classifiers for disease classification even though they are not trained to directly minimize classification error, unlike other predictive models, which is consistent with prior work on real-world clinical data sets. 49,50…”

Section: Resultsmentioning

confidence: 99%

Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method

Gupta¹,

Slater²,

Boyne

et al. 2019

Med Decis Making

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Objectives. Coronary artery disease (CAD) is the leading cause of death and disease burden worldwide, causing 1 in 7 deaths in the United States alone. Risk prediction models that can learn the complex causal relationships that give rise to CAD from data, instead of merely predicting the risk of disease, have the potential to improve transparency and efficacy of personalized CAD diagnosis and therapy selection for physicians, patients, and other decision makers. Methods. We use Bayesian networks (BNs) to model the risk of CAD using the Z-Alizadehsani data set—a published real-world observational data set of 303 Iranian patients at risk for CAD. We also describe how BNs can be used for incorporation of background knowledge, individual risk prediction, handling missing observations, and adaptive decision making under uncertainty. Results. BNs performed on par with machine-learning classifiers at predicting CAD and showed better probability calibration. They achieved a mean 10-fold area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.04, which was comparable with the performance of logistic regression with L1 or L2 regularization (AUC: 0.92 ± 0.06), support vector machine (AUC: 0.92 ± 0.06), and artificial neural network (AUC: 0.91 ± 0.05). We describe the use of BNs to predict with missing data and to adaptively calculate prognostic values of individual variables under uncertainty. Conclusion. BNs are powerful and versatile tools for risk prediction and health outcomes research that can complement traditional statistical techniques and are particularly useful in domains in which information is uncertain or incomplete and in which interpretability is important, such as medicine.

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Section: Resultsmentioning

confidence: 99%

Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method

Gupta¹,

Slater²,

Boyne

et al. 2019

Med Decis Making

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“…Bayesian networks (BN) [24,25,31] are a type of probabilistic graphical model that can be used to build models from data and/or expert opinion. BN is also called a directed acyclic graph or DAG.…”

Section: Methodsmentioning

confidence: 99%

“…However, identification of biomarkers, coupled with their functional analysis may not satisfy the query regarding the causality behind these potential biomarkers and drug’s activity. To understand the system-level regulatory mechanism, here, bayesian network (BN) analysis [24,25] has been employed which developed a probabilistic model of the network with structural characteristics and directed acyclic networks in two different classes: responders vs non-responders so as to enlighten the regulatory map behind the scene.…”

Section: Introductionmentioning

confidence: 99%

Fast and robust method for drug response biomarker identification and sample stratification

Das¹,

Bhattacharya²

2019

Preprint

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With unprecedented progress of cancer research, the world is now prepared with versatile arsenal of drugs to combat cancer. However, response of an individual to any drug or combination treatment stands as a major challenge and hence there exists the sheer need for personalized medication. Identification of drug response biomarkers from a wholistic tumor microenvironment analysis would guide researchers to develop custom-tailored treatment regimen. In this study, a fast and robust method has been developed to identify drug response biomarkers from entire transcriptomics data analysis in a data-driven manner. The biomarkers which were identified by the method, were able to stratify patients between responders vs non-responders population. Furthermore, bayesian network (BN) analysis, done on the data, brought forth a mechanistic insight into the role of identified biomarkers in regulating drug efficacy. The importance of this work lies with the protocol that is time saving and requires less computation power, yet analyzes a whole system data and helps the researchers to take a step forward towards the development of personalized care in effective cancer treatment.

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“…Although using BNs has produced positive results in cancer and disease prediction, this algorithm is not always the best tool. In using BN to predict breast cancer recurrence in women in the Netherlands Cancer Registry compared to other statistical methods, including logistic regression, for risk prediction, researchers found that logistic regression was just as accurate if not more accurate at predicting compared to their developed BN [ 90 ].…”

Section: Machine Learning—a Keystone That Paves the Way For Precision Oncologymentioning

confidence: 99%

Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

Baptiste

Moinuddeen

Soliz

et al. 2021

Genes

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Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person’s variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.

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Comparison of Logistic Regression and Bayesian Networks for Risk Prediction of Breast Cancer Recurrence

Cited by 37 publications

References 50 publications

Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method

Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method

Fast and robust method for drug response biomarker identification and sample stratification

Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

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