An improved breast cancer disease prediction system using ML and PCA

Laghmati, Sara; Hamida, Soufiane; Hicham, Khadija; Cherradi, Bouchaib; Tmiri, Amal

doi:10.1007/s11042-023-16874-w

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2024

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Cited by 18 publications

References 52 publications

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Automated end-to-end Architecture for Retinal Layers and Fluids Segmentation on OCT B-scans

Daanouni,

Cherradi,

Tmiri

2024

Multimed Tools Appl

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Automated end-to-end Architecture for Retinal Layers and Fluids Segmentation on OCT B-scans

Daanouni,

Cherradi,

Tmiri

2024

Multimed Tools Appl

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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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