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

Tian, Jian-xue; Zhang, Jue

doi:10.3934/mbe.2022102

Cited by 15 publications

(9 citation statements)

References 32 publications

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“…As a machine learning method, decision trees have demonstrated numerous applications in medicine and public health, particularly in addressing various issues within the field of cancer. 25 – 27 Such applications span an array of cancer types, including breast, 28 – 32 gastric, 33 , 34 thyroid, 35 prostate, 36 and colorectal cancer. 37 These studies have exhibited significant improvements in the accuracy of cancer diagnoses.…”

Section: Artificial Intelligence Application To Cancer Researchmentioning

confidence: 99%

“… 37 These studies have exhibited significant improvements in the accuracy of cancer diagnoses. 30 , 33 Additionally, the k-means machine learning algorithm has been employed in several types of cancer, such as breast 38 , 39 and skin cancer. 40 Another technique, K-nearest neighbors (KNN), has also been used in cancer research, 41 yielding enhanced accuracy in cancer prediction.…”

Section: Artificial Intelligence Application To Cancer Researchmentioning

confidence: 99%

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The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

Zadeh Shirazi,

Tofighi,

Gharavi

et al. 2024

Technol Cancer Res Treat

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Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care.

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Section: Artificial Intelligence Application To Cancer Researchmentioning

confidence: 99%

Section: Artificial Intelligence Application To Cancer Researchmentioning

confidence: 99%

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

Zadeh Shirazi,

Tofighi,

Gharavi

et al. 2024

Technol Cancer Res Treat

View full text Add to dashboard Cite

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“…They found ANN is the most suitable prediction technique. Tian et al (6) have proposed a method named P-Boosted C5.0 algorithms that combines Principal Component Analysis (PCA), a boosted C5.0 decision tree (DT) algorithm, and penalty factor. PCA was utilised to reduce the dimension of the feature subset.…”

Section: Literature Reviewmentioning

confidence: 99%

An Expert System for Breast Cancer Prediction (ESBCP) using Decision Tree

Das¹,

Biswas²,

Mandal³

2022

IJST

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Objectives: Breast cancer is one of the major concerns in present day scenario. Detecting breast cancer at early stage increases the chances of survival. The objective of this research is to propose suitable feature selection method to improve the efficiency of breast cancer prediction at early stages to increase the survival rate. Methods: In this work, an expert intelligent technique has been proposed named "Expert System for Breast Cancer Prediction (ESBCP)" to detect breast cancer. To validate the results, the proposed system determines accuracy, precision, F-measure, and recall. The proposed model introduced a feature selection technique named -Undiluted Feature Set (UFS) to select the most relevant and promising features. The experimental work was carried out using Python 2.8 version in a Windows environment, taking a dataset on breast cancer from the UCI machine learning repository. There were 699 occurrences in the dataset with nine attributes and two classes. The proposed work utilized a decision tree and a new feature selection technique based on a heuristic search and the Stochastic Hill method. The experimental results were evaluated using the 10-fold Cross-Validation (CV). Findings: The experimental findings showed that the suggested model -ESBCP can accurately detect breast cancer at an early stage. As per the result, with simple decision tree the accuracy recorded 93.42 percent whereas ESBCP obtained 94.01 percent. It may seem that the improvement of 0.59 percent is very small, but for a large population even this mere change can have a greater impact. Novelty: The suggested model ESBCP and the feature selection technique -UFS have a lot of potential in the fields of medical research and bioinformatics in terms of classification capability and predictive power.

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“…15 With the development of machine learning, both the decision tree and the BP neural network model have been widely used in disease recognition and differentiation. 16,17 To the best of our knowledge, only a few studies have investigated the clinical application of a decision tree and BP neural network in the diagnosis of cervical lymphadenopathy. The aim of the present study was to propose and evaluate a decision tree and BP neural network model based on grayscale and color Doppler US features that could be efficiently applied in the differential diagnosis of cervical lymphadenopathy.…”

mentioning

confidence: 99%

“…Back propagation (BP) neural network is an artificial neural network model based on error BP 15 . With the development of machine learning, both the decision tree and the BP neural network model have been widely used in disease recognition and differentiation 16,17 . To the best of our knowledge, only a few studies have investigated the clinical application of a decision tree and BP neural network in the diagnosis of cervical lymphadenopathy.…”

mentioning

confidence: 99%

Development, Validation, and Comparison of 2 Ultrasound Feature–Guided Machine Learning Models to Distinguish Cervical Lymphadenopathy

Zhong¹,

Wang²,

Chen

et al. 2023

Ultrasound Quarterly

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The objective of this study is to develop and validate the performance of 2 ultrasound (US) feature–guided machine learning models in distinguishing cervical lymphadenopathy. We enrolled 705 patients whose US characteristics of lymph nodes were collected at our hospital. B-mode US and color Doppler US features of cervical lymph nodes in both cohorts were analyzed by 2 radiologists. The decision tree and back propagation (BP) neural network were developed by combining clinical data (age, sex, and history of tumor) and US features. The performance of the 2 models was evaluated by calculating the area under the receiver operating characteristics curve (AUC), accuracy value, precision value, recall value, and balanced F score (F1 score). The AUC of the decision tree and BP model in the modeling cohort were 0.796 (0.757, 0.835) and 0.854 (0.756, 0.952), respectively. The AUC, accuracy value, precision value, recall value, and F1 score of the decision tree in the validation cohort were all higher than those of the BP model: 0.817 (0.786, 0.848) vs 0.674 (0.601, 0.747), 0.774 (0.737, 0.811) vs 0.702 (0.629, 0.775), 0.786 (0.739, 0.833) vs 0.644 (0.568, 0.720), 0.733 (0.694, 0.772) vs 0.630 (0.542, 0.718), and 0.750 (0.705, 0.795) vs 0.627 (0.541, 0.713), respectively. The US feature–guided decision tree model was more efficient in the diagnosis of cervical lymphadenopathy than the BP model.

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

Cited by 15 publications

References 32 publications

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

An Expert System for Breast Cancer Prediction (ESBCP) using Decision Tree

Development, Validation, and Comparison of 2 Ultrasound Feature–Guided Machine Learning Models to Distinguish Cervical Lymphadenopathy

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