Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

<p>Today, cancer counted as the riskier disease than the other diseases in the globe. There are many cancer forms like leukemia, skin cancer, and stomach cancer but lung and breast cancer are the most common forms that many people suffered from. Cancer is the disease that cell has grown rapidly and abnormally that is why treating it is somehow tough in some cases but it can be controlled if it is detected in the initial stage. Data-mining classification algorithms had a vital role in predicting and recognizing both benign and malignant cell. Several classifiers are available to classify the usual and unusual cells such as decision-tree, artificial-neural net, SVM, and KNN. This paper presents a systematic review about the most well-known data-mining classification algorithms for lung and breast cancer diagnose. A brief review about KDD and the data-mining concept has demonstrated. The Decision-Tree (D-Tree), ANN, Support-vector-machine, and naïve Bayes classifier that is widely utilized in the biomedical field has been reviewed along with the some algorithms such as C4.5, Cart, and Iterative -Dichotomiser 3 ‘ID3’. A comparison has been done among various reviewed papers in terms of accuracy that used various data-mining classification algorithms to propose the lung and breast cancer diagnosis system. The experimental results of the reviewed papers showed that the Multilayer Perceptron (MLP) and Logistic Regression (LR) gave a higher accuracy of 99.04% and 98.1%, respectively.</p>

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Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis

International Journal on Advanced Science Engineering and Information Technology ◽

10.18517/ijaseit.11.2.12955 ◽

2021 ◽

Vol 11 (2) ◽

pp. 508

Author(s):

Mohanad A. Deif ◽

Rania E. Hammam ◽

Ahmed A. A. Solyman

Keyword(s):

Breast Cancer ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis ◽

Gradient Boosting ◽

Gradient Boosting Machine

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Breast cancer diagnosis using feature extraction and boosted C5.0 decision tree algorithm with penalty factor

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2022102 ◽

2022 ◽

Vol 19 (3) ◽

pp. 2193-2205

Author(s):

Jian-xue Tian ◽

◽

Jue Zhang

Keyword(s):

Breast Cancer ◽

Feature Extraction ◽

Decision Tree ◽

Cancer Diagnosis ◽

Class Imbalance ◽

Breast Cancer Diagnosis ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Penalty Factor ◽

C5.0 Decision Tree

<abstract><p>To overcome the two class imbalance problem among breast cancer diagnosis, a hybrid method by combining principal component analysis (PCA) and boosted C5.0 decision tree algorithm with penalty factor is proposed to address this issue. PCA is used to reduce the dimension of feature subset. The boosted C5.0 decision tree algorithm is utilized as an ensemble classifier for classification. Penalty factor is used to optimize the classification result. To demonstrate the efficiency of the proposed method, it is implemented on biased-representative breast cancer datasets from the University of California Irvine(UCI) machine learning repository. Given the experimental results and further analysis, our proposal is a promising method for breast cancer and can be used as an alternative method in class imbalance learning. Indeed, we observe that the feature extraction process has helped us improve diagnostic accuracy. We also demonstrate that the extracted features considering breast cancer issues are essential to high diagnostic accuracy.</p></abstract>

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