Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application

Akbulut, Sami; ÇİÇEK, İpek BALIKÇI; Çolak, Cemil

doi:10.4274/haseki.galenos.2022.8440

Cited by 10 publications

(9 citation statements)

References 22 publications

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“…The mainly horizontal growth of the LightGBM model prevents over-learning. As a result, large datasets produce better results [46].…”

Section: ) Light Gradient Boosting Machine (Lightgbm)mentioning

confidence: 99%

Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm

Batool,

Byun

2024

IEEE Access

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Over the past decade, breast cancer has been the most common type of cancer in women. Different methods were proposed for breast cancer detection. These methods mainly classify and categorize malignant and Benign tumors. Machine learning is a practical approach for breast cancer classification. Data mining and classification are effective methods to predict and categorize breast cancer. The optimum classification for detecting Breast Cancer (BC) is ensemble-based. The ensemble approach involves using multiple ways to find the best possible solution. This study used the Wisconsin Breast Cancer Diagnostic (WBCD) dataset. We created a voting ensemble classifier that combines four different machine learning models: Extra Trees Classifier (ETC), Light Gradient Boosting Machine (LightGBM), Ridge Classifier (RC), and Linear Discriminant Analysis (LDA). The proposed ELRL-E approach achieved an accuracy of 97.6%, a precision of 96.4%, a recall of 100%, and an F1 score of 98.1%. Various output evaluations are used to evaluate the performance and efficiency of the proposed model and other classifiers. Overall, the recommended strategy performed better. Results are directly compared with the individual classifier and different recognized state-of-the-art classifiers. The primary objective of this study is to identify the most influential ensemble machine learning classifier for breast cancer detection and diagnosis in terms of accuracy and AUC score.

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“…The mainly horizontal growth of the LightGBM model prevents over-learning. As a result, large datasets produce better results [46].…”

Section: ) Light Gradient Boosting Machine (Lightgbm)mentioning

confidence: 99%

Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm

Batool,

Byun

2024

IEEE Access

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“…Um dos pontos no qual esse classificador difere dos demais algoritmos baseados em GBDT é a forma da construc ¸ão das árvores de decisão. O LightGBM usa a estratégia leaf-wise com limitac ¸ão de profundidade para dividir as árvores focando nas folhas que reduzem a func ¸ão de erro, ao invés das técnicas tradicionais que fazem uso de algoritmos level-wise, o que previne overlearning dado que o crescimento do modelo é horizontal e as árvores de decisão são menos profundas [4].…”

Section: B Algoritmos De Classificac ¸ãOunclassified

“…O carcinoma da mama é uma patologia considerada complexa devido a sua diversidade de alterac ¸ões moleculares, composic ¸ões celulares e de comportamentos clínicos, que podem ser relacionados à mudanc ¸as nas expressões genéticas associadas [3], [4]. Isto porque o desenvolvimento e crescimento de neoplasias malignas no tecido da mama é influenciado por fatores genéticos que incluem disparidades de expressão e mutac ¸ões em genes de predisposic ¸ão ao câncer de alto e moderado risco [5].…”

Section: Introduc ¸ãOunclassified

“…Entre estas técnicas, os de Gradient boosting decision tree (GBDT) criam modelos robustos ao sequenciar classificadores mais simples iterativamente, de forma que cada uma de suas árvores de decisão otimiza os pesos da func ¸ão de custo com base nos erros do classificador anterior [9]. O trabalho apresentado em [4] utilizou os algoritmos de gradient boosting machine (GBM), extreme gradient boosting (XGBoost) e light gradient boosting (LightGBM) para classificar dados do Breast Cancer Wisconsin Dataset [https://archive.ics.uci.edu/datasets] que conta com 10 características para o BRCA. Os resultados obtidos mostraram que o LightGBM exibiu melhor performance com acurácia de 95, 3%.…”

Section: Introduc ¸ãOunclassified

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Obtenção de novos potenciais biomarcadores para o câncer de mama com o auxílio da inteligência artificial explicável

Souza,

Azevedo,

Dalmolin

et al. 2023

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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O câncer de mama é a neoplasia maligna mais comum em ambos os sexos, representando um quarto dos diagnósticos de câncer em mulheres. Para melhorar a identificação e o desenvolvimento de terapias mais eficazes, e fundamental encontrar potenciais biomarcadores de prognostico. Neste estudo, uma abordagem foi desenvolvida utilizando técnicas de aprendizado de máquina e inteligência artificial explicável para identificar genes relevantes na distinção entre câncer de mama é tecido normal. Os resultados deste estudo revelaram um total de 74 genes com potencial importância na classificação. Algumas características desses genes mostraram evidencias de influenciar a expressão do câncer de mama, tornando-os possíveis biomarcadores prognósticos. Essa abordagem inovadora, que combina aprendizado de máquina e inteligência artificial explicável, pode fornecer insights valiosos sobre os mecanismos moleculares subjacentes ao câncer de mama. A identificação de genes com relevancia na classificação pode contribuir para o desenvolvimento de terapias mais direcionadas e personalizadas, melhorando assim o prognostico e o tratamento dos pacientes com câncer de mama.

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“…Akbulut et al [ 22 ] performed the breast cancer classification using machine learning algorithms. The authors used three different machine learning models such as GBM, XGBoost, and LightGBM for breast cancer classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm

Umer

Naveed

Alrowais

et al. 2022

Cancers

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Breast cancer is a common cause of female mortality in developing countries. Screening and early diagnosis can play an important role in the prevention and treatment of these cancers. This study proposes an ensemble learning-based voting classifier that combines the logistic regression and stochastic gradient descent classifier with deep convoluted features for the accurate detection of cancerous patients. Deep convoluted features are extracted from the microscopic features and fed to the ensemble voting classifier. This idea provides an optimized framework that accurately classifies malignant and benign tumors with improved accuracy. Results obtained using the voting classifier with convoluted features demonstrate that the highest classification accuracy of 100% is achieved. The proposed approach revealed the accuracy enhancement in comparison with the state-of-the-art approaches.

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Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application

Cited by 10 publications

References 22 publications

Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm

Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm

Obtenção de novos potenciais biomarcadores para o câncer de mama com o auxílio da inteligência artificial explicável

Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm

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