Assessment of Bayesian Network Classifiers as Tools for Discriminating Breast Cancer Pre-diagnosis Based on Three Diagnostic Methods

Yaneli, Ameca-Alducin María; Cruz-Ramírez, Nicandro; Mezura-Montes, Efrén; Enrique, Martín-Del-Campo-Mena; Pérez-Castro, Nancy; Gabriel, Acosta-Mesa Héctor

doi:10.1007/978-3-642-37807-2_36

Cited by 8 publications

(1 citation statement)

References 23 publications

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“…Thermography was used by Yaneli et al [55] for the pre-diagnosis of breast cancer, based on the predictive value of its attributes. A database of 98 patients was included in the BN used.…”

Section: Comprehensive Review Of Bayesian Network On Some Diseasesmentioning

confidence: 99%

Bayesian Networks for the Diagnosis and Prognosis of Diseases: A Scoping Review

Polotskaya,

Muñoz-Valencia,

Rabasa

et al. 2024

MAKE

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Bayesian networks (BNs) are probabilistic graphical models that leverage Bayes’ theorem to portray dependencies and cause-and-effect relationships between variables. These networks have gained prominence in the field of health sciences, particularly in diagnostic processes, by allowing the integration of medical knowledge into models and addressing uncertainty in a probabilistic manner. Objectives: This review aims to provide an exhaustive overview of the current state of Bayesian networks in disease diagnosis and prognosis. Additionally, it seeks to introduce readers to the fundamental methodology of BNs, emphasising their versatility and applicability across varied medical domains. Employing a meticulous search strategy with MeSH descriptors in diverse scientific databases, we identified 190 relevant references. These were subjected to a rigorous analysis, resulting in the retention of 60 papers for in-depth review. The robustness of our approach minimised the risk of selection bias. Results: The selected studies encompass a wide range of medical areas, providing insights into the statistical methodology, implementation feasibility, and predictive accuracy of BNs, as evidenced by an average area under the curve (AUC) exceeding 75%. The comprehensive analysis underscores the adaptability and efficacy of Bayesian networks in diverse clinical scenarios. The majority of the examined studies demonstrate the potential of BNs as reliable adjuncts to clinical decision-making. The findings of this review affirm the role of Bayesian networks as accessible and versatile artificial intelligence tools in healthcare. They offer a viable solution to address complex medical challenges, facilitating timely and informed decision-making under conditions of uncertainty. The extensive exploration of Bayesian networks presented in this review highlights their significance and growing impact in the realm of disease diagnosis and prognosis. It underscores the need for further research and development to optimise their capabilities and broaden their applicability in addressing diverse and intricate healthcare challenges.

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“…Thermography was used by Yaneli et al [55] for the pre-diagnosis of breast cancer, based on the predictive value of its attributes. A database of 98 patients was included in the BN used.…”

Section: Comprehensive Review Of Bayesian Network On Some Diseasesmentioning

confidence: 99%

Bayesian Networks for the Diagnosis and Prognosis of Diseases: A Scoping Review

Polotskaya,

Muñoz-Valencia,

Rabasa

et al. 2024

MAKE

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Comparison of Statistical, LBP, and Multi-Resolution Analysis Features for Breast Mass Classification

2014

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Millions of women are suffering from breast cancer, which can be treated effectively if it is detected early. Mammography is broadly recognized as an effective imaging modality for the early detection of breast cancer. Computer-aided diagnosis (CAD) systems are very helpful for radiologists in detecting and diagnosing abnormalities earlier and faster than traditional screening programs. An important step of a CAD system is feature extraction. This research gives a comprehensive study of the effects of different features to be used in a CAD system for the classification of masses. The features are extracted using local binary pattern (LBP), which is a texture descriptor, statistical measures, and multi-resolution frameworks. Statistical and LBP features are extracted from each region of interest (ROI), taken from mammogram images, after dividing it into N×N blocks. The multi-resolution features are based on discrete wavelet transform (DWT) and contourlet transform (CT). In multi-resolution analysis, ROIs are decomposed into low sub-band and high sub-bands at different resolution levels and the coefficients of the low sub-band at the last level are taken as features. Support vector machines (SVM) is used for classification. The evaluation is performed using Digital Database for Screening Mammography (DDSM) database. An accuracy of 98.43 is obtained using statistical or LBP features but when both these types of features are fused, the accuracy is increased to 98.63. The CT features achieved classification accuracy of 98.43 whereas the accuracy resulted from DWT features is 96.93. The statistical analysis and ROC curves show that methods based on LBP, statistical measures and CT performs equally well and they not only outperform DWT based method but also other existing methods.

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