Gaussian Mixture Models for Probabilistic Classification of Breast Cancer

Prabakaran, Indira; Wu, Zhengdong; Lee, Chang-Gun; Tong, Brian; Steeman, Samantha; Koo, Gabriel; Zhang, Paul J.; Guvakova, Marina A.

doi:10.1158/0008-5472.can-19-0573

Cited by 29 publications

(18 citation statements)

References 45 publications

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“…Although the GMM has been successfully applied in the molecular stratification of patients with breast cancer [33], it failed to recover the cluster structure identified by HAC, SOM, and K-means. A possible explanation is that model-based clustering assumes that the underlying model is correctly specified, and each data cluster can be viewed as a sample from a mixture component.…”

Section: Discussionmentioning

confidence: 99%

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Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis

Ferro

Bottigliengo

Gregori

et al. 2021

JPM

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Primary breast cancer (PBC) is a heterogeneous disease at the clinical, histopathological, and molecular levels. The improved classification of PBC might be important to identify subgroups of the disease, relevant to patient management. Machine learning algorithms may allow a better understanding of the relationships within heterogeneous clinical syndromes. This work aims to show the potential of unsupervised learning techniques for improving classification in PBC. A dataset of 712 women with PBC is used as a motivating example. A set of variables containing biological prognostic parameters is considered to define groups of individuals. Four different clustering methods are used: K-means, self-organising maps, hierarchical agglomerative (HAC), and Gaussian mixture models clustering. HAC outperforms the other clustering methods. With an optimal partitioning parameter, the methods identify two clusters with different clinical profiles. Patients in the first cluster are younger and have lower values of the oestrogen receptor (ER) and progesterone receptor (PgR) than patients in the second cluster. Moreover, cathepsin D values are lower in the first cluster. The three most important variables identified by the HAC are: age, ER, and PgR. Unsupervised learning seems a suitable alternative for the analysis of PBC data, opening up new perspectives in the particularly active domain of dissecting clinical heterogeneity.

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Section: Discussionmentioning

confidence: 99%

“…The observations can be then assigned to the clusters with the highest membership probability. A limited number of studies used GMMs to define breast cancer patients' groups based on their characteristics [33].…”

Section: Gaussian Mixture Model (Gmm)mentioning

confidence: 99%

Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis

Ferro

Bottigliengo

Gregori

et al. 2021

JPM

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“…Currently, classifiers mainly include two types based on machine learning and deep learning. In the field of machine learning, the commonly used classification models mainly include the Gaussian model [23][24], SVM [25][26], AdaBoost [27][28], and fuzzy system [29][30]. The use of machine learning algorithms often needs to be equipped with suitable feature extraction methods to get the desired results.…”

Section: Introductionmentioning

confidence: 99%

A Violation Information Recognition Method of Live‐Broadcasting Platform Based on Machine Learning Technology

Shen

Yuan

2021

Wireless Communications and Mobile Computing

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With the development of the live broadcast industry, security issues in the live broadcast process have become increasingly apparent. At present, the supervision of various live broadcast platforms is basically in a state of human supervision. Manpower supervision is mainly through user reporting and platform supervision measures. However, there are a large number of live broadcast rooms at the same time, and only relying on human supervision can no longer meet the monitoring needs of live broadcasts. Based on this situation, this study proposes a violation information recognition method of a live-broadcasting platform based on machine learning technology. By analyzing the similarities and differences between normal live broadcasts and violation live broadcasts, combined with the characteristics of violation image data, this study mainly detects human skin color and sensitive parts. A prominent feature of violation images is that they contain a large area of naked skin, and the ratio of the area of naked skin to the overall image area of the violation image will exceed the threshold. Skin color recognition plays a role in initial target positioning. The accuracy of skin color recognition is directly related to the recognition accuracy of the entire system, so skin color recognition is the most important part of violation information recognition. Although there are many effective skin color recognition technologies, the accuracy and stability of skin color recognition still need to be improved due to the influence of various external factors, such as light intensity, light source color, and physical equipment. When it is detected that the area of the skin color in the live screen exceeds the threshold, it is preliminarily determined to be a suspected violation video. In order to improve the recognition accuracy, it is necessary to detect sensitive parts of the suspected video. Naked female breasts are a very obvious feature in violation images. This study uses a chest feature extraction method to detect the chest in the image. When the recognition result is a violation image, it is determined that the live broadcast involves violation content. The machine learning algorithm is simple to implement, and the parameters are easy to adjust. The classifier training requires a short time and is suitable for live violation information recognition scenarios. The experimental results on the adopted data set show that the method used in this article can effectively detect videos with violation content. The recognition rate is as high as 85.98%, which is suitable for a real-life environment and has good practical significance.

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“…Gaussian mixture model classifier, GMM, has been proved to be effective in many applications ( [17], [18], [19]). However, in high dimension, GMM maybe not that effective.…”

Section: Introductionmentioning

confidence: 99%

Parkinson’s Disease Classification using Gaussian Mixture Models with Relevance Feature Weights on Vocal Feature Sets

Bchir¹

2020

IJACSA

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In order to perceive automatically the manifestation of dysarthria in Parkinson's disease, we propose a novel classifier which is able to categorize acoustic features and detects articulatory deficits. The proposed approach incorporates relevance feature weighting to the Gaussian mixture model in order to address the issue of high dimensionality. Besides, it learns the relevance feature weights with respect to each model along with the Gaussian mixture model parameters to deal with the specificity of the class models. In order to assess the performance of the proposed approach, we used the data collected by the department of neurology in Cerrahpaşa faculty of medicine at Istanbul University. The obtained results of the Gaussian mixture models with relevance feature weights algorithm are first compared to the GMM results, and to the most recent related work. The experimental results showed the effectiveness of the proposed approach with an accuracy of 0.89 and an MCC score of 0.7.

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Gaussian Mixture Models for Probabilistic Classification of Breast Cancer

Cited by 29 publications

References 45 publications

Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis

Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis

A Violation Information Recognition Method of Live‐Broadcasting Platform Based on Machine Learning Technology

Parkinson’s Disease Classification using Gaussian Mixture Models with Relevance Feature Weights on Vocal Feature Sets

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