Holo entropy enabled decision tree classifier for breast cancer diagnosis using wisconsin (prognostic) data set

Sayed, Shabina; Ahmed, S.; Poonia, Rakesh

doi:10.1109/csnt.2017.8418532

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…Literature performed latent feature learning for different modalities and mapped the features to the label space to complete AD diagnosis [18]. Literature used a sparse deep polynomial network 2 BioMed Research International (S-DPN) to complete multimodal data fusion to obtain new features with more robust discrimination [19]. Some scholars also use hypergraphs to complete high-order correlation analysis between multimodal data and generate high-quality features [20,21].…”

Section: Feature Optimizationmentioning

confidence: 99%

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection

Bhende¹,

Thakare²,

Pant

et al. 2022

BioMed Research International

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Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization. The multiview effective region gene optimization (MvERGS) algorithm is designed to refine the original image features, improve the feature discriminate and compress the feature dimension, better match the number of samples, and perform discriminate correlation analysis (DCA) on the advanced new features; in-depth cross-modal correlation between heterogeneous elements, that is, the deep pathological information, can be mined to describe the breast mass lesion area accurately. Based on deep pathological information and traditional classifiers, an efficient breast mass recognition model is trained to complete the classification of mammography images. Experiments show that the key technical indicators of the recognition model, including accuracy and AUC, are better than the mainstream baselines, and the overfitting problem caused by the scarcity of samples is alleviated.

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Section: Feature Optimizationmentioning

confidence: 99%

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection

Bhende¹,

Thakare²,

Pant

et al. 2022

BioMed Research International

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“…In the study, CNN is used for feature extraction and the support vector machine is employed for prediction of the breast cancer. In [13], KNN based breast cancer prediction model is proposed. The dataset consists of 209 observations collected manually by the authors.…”

Section: Litreature Reviewmentioning

confidence: 99%

Breast cancer prediction model with decision tree and adaptive boosting

Assegie

Tulasi²,

Kumar

2021

IJ-AI

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In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.

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“…Diagnosis of breast cancer work is carried out by employing Holo entropy enabled decision tree classifier with Wisconsin dataset[17]. The hole entropy is estimated for every feature occur in the dataset and the attribute which carry the maximum rate of holo entropy[17] is elected just as preeminent attribute. The accuracy is calculated for each chunk size by building DT model.…”

mentioning

confidence: 99%

Prognostic Analysis of Machine Learning Techniques for Breast Cancer Detection

HL,

Kumar SP,

et al. 2022

Int J Bioinfor Intell Comput

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Later lung cancer, breast cancer is the casual nosy cancer and it is the second dominant root of cancer demise in women. Cancer is when the mutations that occurs in genes regulate cell growth and mutations multiply and divide the cells in an undisciplined way. There are five stages of breast cancer. In each stage, the size of the tumor varies. Alcohol consumption, body weight, history of breast cancer, age, genetics, hormone treatments, etc. are the reasons for breast cancer. Two categories of breast cancer are Lobular and Ductal. Ultrasound, MRI, Mammogram are the several diagnosis methods. By employing Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), decision tree (DT), Random forest (RFA), Naïve Bayes ī (NB), gradient boosting (GB), Logistic regression (LR) and Support Vector Machine (SVM) breast cancer can be predicted. The model gives best results when the principal features are selected.

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Holo entropy enabled decision tree classifier for breast cancer diagnosis using wisconsin (prognostic) data set

Cited by 8 publications

References 11 publications

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection

Breast cancer prediction model with decision tree and adaptive boosting

Prognostic Analysis of Machine Learning Techniques for Breast Cancer Detection

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