Using Resistin, glucose, age and BMI to predict the presence of breast cancer

Patrício, Miguel; Pereira, José Carlos Seabra; Crisóstomo, Joana; Matafome, Paulo; Gomes, Maria Rosemary da Silva; Seiça, Raquel; Caramelo, Francisco

doi:10.1186/s12885-017-3877-1

Cited by 221 publications

(208 citation statements)

References 29 publications

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“…For this task, we made predictions for two instances (persons) utilizing our trained Grey-Box model and now we attempt to explain, in understandable to every human terms, why our model made these predictions. Table 3 presents the values of each attribute for two instances of the Coimbra dataset while a detailed description of the attributes can be found in [24]. Figure 15 presents the trained Grey-Box model's inner structure.…”

Section: Discussion About the Interpretability Of The Grey-box Modelmentioning

confidence: 99%

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A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability

2020

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Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to make very accurate predictions and decisions on a wide range of applications. Nevertheless, we still seek to understand and explain how these models work and make decisions. Explainability and interpretability in machine learning is a significant issue, since in most of real-world problems it is considered essential to understand and explain the model's prediction mechanism in order to trust it and make decisions on critical issues. In this study, we developed a Grey-Box model based on semi-supervised methodology utilizing a self-training framework. The main objective of this work is the development of a both interpretable and accurate machine learning model, although this is a complex and challenging task. The proposed model was evaluated on a variety of real world datasets from the crucial application domains of education, finance and medicine. Our results demonstrate the efficiency of the proposed model performing comparable to a Black-Box and considerably outperforming single White-Box models, while at the same time remains as interpretable as a White-Box model.

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Section: Discussion About the Interpretability Of The Grey-box Modelmentioning

confidence: 99%

“…The Coimbra dataset [24] is comprised by ten attributes, all quantitative and a binary dependent variable which indicates the presence or absence of breast cancer. All clinical features were measured for 64 patients with breast cancer and 52 healthy controls.…”

Section: Description Of Datasetsmentioning

confidence: 99%

A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability

2020

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“…The following data set is also devoted to the problems of forecasting breast diseases (Crisóstomo et al, 2016), (Patrício et al, 2018). The data set contains 116 points in a 10dimensional attribute space.…”

Section: Figure 8 Extension Of Elastic Map For Classes Con Adimentioning

confidence: 99%

“…Attribute space contains ten predictors. According to (Patrício et al, 2018) the predictors are anthropometric data and parameters which can be gathered in routine blood analysis. Prediction models based on these predictors, if accurate, can potentially be used as a biomarker of breast cancer.…”

Section: Figure 8 Extension Of Elastic Map For Classes Con Adimentioning

confidence: 99%

The Procedures of Visual Analysis for Multidimensional Data Volumes

Bondarev

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The paper is devoted to problems of visual analysis of multidimensional data sets using an approach based on the construction of elastic maps. This approach is quite suitable for processing and visualizing of multidimensional datasets. The elastic maps are used as the methods of original data points mapping to enclosed manifolds having less dimensionality. Diminishing the elasticity parameters one can design map surface which approximates the multidimensional dataset in question much better. Then the points of dataset in question are projected to the map. The extension of designed map to a flat plane allows one to get an insight about the structure of multidimensional dataset. The paper presents the results of applying elastic maps for visual analysis of multidimensional data sets of medical origin. Previously developed data processing procedures are applied to improve the results obtained - pre-filtering of data, removal of separated clusters (flotation), quasi-Zoom.</p>

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“…Protected personal health information was removed for the purpose of this research. Because it analyzed de-identified data, this study was excepted from ethics review by the Ethical Committee of CHUC and all participants gave their written informed consent prior to entering the study [7].…”

Section: Data Collectionmentioning

confidence: 99%

Machine Learning Models of Breast Cancer Risk Prediction

Islam

Poly

2019

Preprint

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Breast cancer is the most common cancer in women both in the developed and less developed world. Early detection based on clinical features can greatly increase the chances for successful treatment. Our goal was to construct a breast cancer prediction model based on machine learning algorithms. A total of 10 potential clinical features like age, BMI, glucose, insulin, HOMA, leptin, adiponectin, resistin, and MCP-1 were collected from 116 patients. In this report, most commonly used machine learning model such as decision tree (DT), random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models were tested for breast cancer prediction. A repeated 10fold cross-validation model was used to rank variables on the randomly split dataset. The accuracy of DT, RF, SVM, LR, ANN, and KNN was 0.71, 0.71, 0.77, 0.80, 0.81, and 0.86 respectively. However, The KNN model showed most higher accuracy with area under receiver operating curve, sensitivity, and specificity of 0.95, 0.80, 0.91. Therefore, identification of breast cancer patients correctly would create care opportunities such as monitoring and adopting intervention plans may benefit the quality of care in long-term.

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Using Resistin, glucose, age and BMI to predict the presence of breast cancer

Cited by 221 publications

References 29 publications

A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability

A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability

The Procedures of Visual Analysis for Multidimensional Data Volumes

Machine Learning Models of Breast Cancer Risk Prediction

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