4.6 Article

Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

Related references

Note: Only part of the references are listed.
Article Geography, Physical

A robust discretization method of factor screening for landslide susceptibility mapping using convolution neural network, random forest, and logistic regression models

Zheng Zhao et al.

Summary: The aim of this study is to propose a robust discretization criterion (RDC) to quantify and explore the uncertainty and subjectivity of different discretization methods. The results show that the RDC method can extract the more representative features from environmental factors and outperform the other conventional discretization methods.

INTERNATIONAL JOURNAL OF DIGITAL EARTH (2023)

Article Computer Science, Artificial Intelligence

Hybrid PSO feature selection-based association classification approach for breast cancer detection

Bilal Sowan et al.

Summary: Breast cancer is a leading cause of death among women worldwide, and there are challenges in automatic breast cancer diagnosis. This research proposes an ensemble filter feature selection method and a wrapper feature selection algorithm for breast cancer classification, achieving impressive performance.

NEURAL COMPUTING & APPLICATIONS (2023)

Article Computer Science, Artificial Intelligence

Genetic hyperparameter optimization with Modified Scalable-Neighbourhood Component Analysis for breast cancer prognostication

Shtwai Alsubai et al.

Summary: Breast cancer is a common disease among women that can result in mortality if left untreated. Early detection is crucial and traditional detection methods are time-consuming. Data mining can assist in predicting the disease and determining significant attributes for diagnosis. However, traditional techniques have limitations in terms of prediction rate. This study aims to implement a non-parametric strategy for breast cancer diagnosis by optimizing feature embedding rather than using parametric classifiers.

NEURAL NETWORKS (2023)

Article Chemistry, Multidisciplinary

Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks

Javad Hassannataj Joloudari et al.

Summary: Imbalanced Data (ID) is a problem in machine learning where one class has significantly more samples than the other, leading to biased learning. This paper investigates the effectiveness of deep neural networks and convolutional neural networks mixed with oversampling and undersampling methods to handle imbalanced data. The proposed CNN-based model with SMOTE achieves 99.08% accuracy on 24 imbalanced datasets.

APPLIED SCIENCES-BASEL (2023)

Article Computer Science, Interdisciplinary Applications

Artificial intelligence based medical decision support system for early and accurate breast cancer prediction

Law Kumar Singh et al.

Summary: Feature selection is one of the most important aspects of the machine learning area, which picks the optimal subset of characteristics related to the target data by deleting unnecessary data. This study proposes a unique feature selection method based on the Eagle Strategy Optimization (ESO) algorithm, Gravitational Search Optimization (GSO) algorithm, and their hybrid algorithm. The goal of this study is to categorize breast cancer into two groups using the benchmark feature set and to choose the fewest features (feature selection) to achieve maximum accuracy.

ADVANCES IN ENGINEERING SOFTWARE (2023)

Article Oncology

Cancer statistics, 2023

Rebecca L. Siegel et al.

Summary: The American Cancer Society predicts that there will be 1,958,310 new cancer cases and 609,820 cancer deaths in the United States in 2023. While incidence trends for most cancers are favorable, prostate cancer saw a significant increase of 3% annually from 2014 to 2019. However, the overall cancer death rate continues to decline due to advances in treatment, with a 33% reduction since 1991.

CA-A CANCER JOURNAL FOR CLINICIANS (2023)

Article Engineering, Electrical & Electronic

Effective kernel-principal component analysis based approach for wisconsin breast cancer diagnosis

Zohaib Mushtaq et al.

Summary: This study aims to distinguish between malignant and non-malignant cells in a breast cancer database. The Wisconsin breast cancer data was used, and the Naive Bayes algorithm with Gaussian distribution in combination with Chi-squared-based attribute selection was proposed. The data dimension was reduced using extended Kernel Principal Component Analysis (K-PCA), and five different kernels were tested. The performance of the proposed system was evaluated based on accuracy, precision, sensitivity, and specificity, achieving a best accuracy of 99.28% with six selected features and sigmoid K-PCA. This outperforms many recent state-of-the-art studies on the same dataset.

ELECTRONICS LETTERS (2023)

Article Physics, Multidisciplinary

Homogeneous Adaboost Ensemble Machine Learning Algorithms with Reduced Entropy on Balanced Data

Mahesh Thyluru Ramakrishna et al.

Summary: This paper focuses on predicting and classifying breast cancer using Adaboost ensemble techniques. Experimental findings show that the Adaboost-random forest classifier achieves an accuracy of 97.95% for prediction.

ENTROPY (2023)

Article Computer Science, Information Systems

On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice

Mahdi Aghaabbasi et al.

Summary: Prediction of work travel mode choice is crucial for travel demand forecasting and achieving sustainability goals. Machine learning methods, including support vector machines, k-nearest neighbor, decision trees, and Naive Bayes, have been optimized using the Bayesian Optimization algorithm on two datasets. The findings show that the BO model significantly improves the performance of the k-nearest neighbor model. This research lays the foundation for using optimized machine learning methods to mitigate the negative consequences of automobile use.

IEEE ACCESS (2023)

Article Computer Science, Information Systems

HC-DTTSVM: A Network Intrusion Detection Method Based on Decision Tree Twin Support Vector Machine and Hierarchical Clustering

Li Zou et al.

Summary: Network intrusion detection is crucial for national cyberspace security. This study proposes a method called HC-DTTWSVM which combines decision tree, twin support vector machine, and hierarchical clustering for effective detection of different categories of network intrusion.

IEEE ACCESS (2023)

Article Radiology, Nuclear Medicine & Medical Imaging

Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features

Jing Zhang et al.

Summary: The purpose of this study was to develop an automatic computer-aided diagnosis (CAD) pipeline based on multiparametric magnetic resonance imaging (mpMRI) and investigate the role of different imaging features in the classification of breast cancer. The study included 222 histopathology-confirmed breast lesions and trained a neural network-based lesion segmentation model to extract radiomics features from DWI, T2WI, and DCE parametric maps. Models based on sequence combinations achieved higher diagnostic accuracy compared to BI-RADS scores, and the joint model combining radiomics and BI-RADS scores achieved the highest accuracy.

RADIOLOGIA MEDICA (2023)

Article Engineering, Biomedical

Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach

Mohammad H. Alshayeji et al.

Summary: A shallow artificial neural network model was used in this study to diagnose and predict breast cancer with high accuracy on the WBCD and WDBC datasets, without the need for feature optimization or selection algorithms. The model showed promising performance, demonstrating significant potential for diagnosing breast cancer.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Article Engineering, Electrical & Electronic

Efficient feature subset selection and classification using levy flight-based cuckoo search optimization with parallel support vector machine for the breast cancer data

Krishnamoorthy Sashi Rekha et al.

Summary: In this article, a levy flight-based cuckoo search optimization (LFCSO) with parallel support vector machine (PSVM) technique is proposed to enhance the classification accuracy of breast cancer. The experimental results demonstrate that the proposed LFCSO-PSVM algorithm provides higher efficiency of classification than the existing algorithms in terms of precision, recall, f-measurement, and reliability.

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY (2022)

Article Computer Science, Artificial Intelligence

Improved cost-sensitive multikernel learning support vector machine algorithm based on particle swarm optimization in pulmonary nodule recognition

Yang Li et al.

Summary: This study proposes a cost-sensitive multikernel learning support vector machine (CS-MKL-SVM) algorithm for lung nodule detection in the lung computer-aided detection (Lung CAD) system. The algorithm assigns different penalty coefficients to positive and negative samples, improving the learning and classification of true positive nodules. By introducing a new score function named F-new based on the harmonic mean of accuracy (ACC) and sensitivity (SEN), and using particle swarm optimization (PSO) for parameter optimization, the detection rate and overall recognition accuracy of pulmonary nodules are further improved. Experimental results show that the proposed algorithm outperforms other algorithms in terms of F-new, ACC, and SEN.

SOFT COMPUTING (2022)

Article Computer Science, Information Systems

A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction

Mahmood Khalsan et al.

Summary: This article provides a comprehensive review of recent cancer studies that utilize gene expression data for cancer prediction, tumor identification, and stratification. It also discusses biomarker studies related to various cancer types. The article covers multiple aspects of machine learning in cancer research and highlights some technical issues.

IEEE ACCESS (2022)

Article Multidisciplinary Sciences

Machine learning for multi-omics data integration in cancer

Zhaoxiang Cai et al.

Summary: This article reviews the application of machine learning in multi-omics data analysis, including both general-purpose and task-specific methods. By benchmarking the performance of five machine learning approaches using data from the Cancer Cell Line Encyclopedia, recommendations are provided for method selection in specific applications. The importance of this research lies in promoting the development of novel machine learning methodologies, which will be critical for drug discovery, clinical trial design, and personalized treatments.

ISCIENCE (2022)

Article Computer Science, Artificial Intelligence

An accurate soft diagnosis method of breast cancer using the operative fusion of derived features and classification approaches

Sunil Kumar Jha et al.

Summary: Breast cancer is a common type of cancer worldwide, and early recognition is crucial for treatment. This study proposes an effective transformation approach using data mining and classification methods to accurately identify breast cancer.

EXPERT SYSTEMS (2022)

Article Engineering, Electrical & Electronic

WARM: a new breast masses classification method by weighting association rule mining

Mohammad Reza Keyvanpour et al.

Summary: Breast cancer incidence in women has significantly increased, emphasizing the importance of early detection. This study introduces a new method based on WARM for optimizing the accuracy of mass detection and classification in mammography images.

SIGNAL IMAGE AND VIDEO PROCESSING (2022)

Article Chemistry, Analytical

A Model for Predicting Cervical Cancer Using Machine Learning Algorithms

Naif Al Mudawi et al.

Summary: Machine learning and deep learning are increasingly being used to analyze large amounts of data and generate actionable insights, including predicting the early stages of serious illnesses. This study presents a smart way to predict cervical cancer using machine learning algorithms. Experimental results show that the highest classification score of 100% in cervical cancer prediction is achieved with random forest, decision tree, adaptive boosting, and gradient boosting algorithms. The study also includes a survey of 132 Saudi Arabian volunteers to understand their thoughts on computer-assisted cervical cancer prediction.

SENSORS (2022)

Article Engineering, Multidisciplinary

New Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis

Elsayed Badr et al.

Summary: The study presents a novel approach to improve breast cancer diagnosis accuracy, including enhancing support vector machine performance, introducing new scaling techniques, and utilizing parallel techniques to enhance efficiency.

ALEXANDRIA ENGINEERING JOURNAL (2022)

Article Computer Science, Information Systems

A General Framework for Class Label Specific Mutual Information Feature Selection Method

Deepak Kumar Rakesh et al.

Summary: This article introduces a new feature selection method called class-label specific mutual information (CSMI), which selects a specific subset of features for each class label. The proposed method maximizes the information shared among the selected features and the target class label while minimizing the same with all classes. Experimental results show that the CSMI method outperforms traditional and state-of-the-art ITFS methods in multiple benchmark datasets.

IEEE TRANSACTIONS ON INFORMATION THEORY (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI

Kotaro Yoshida et al.

Summary: The purpose of this study was to investigate if radiomics machine learning based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. The study enrolled 78 patients and created early enhancement mapping images of pretreatment DCE-MRI. Different prediction models were built using various combinations of clinical information, radiological findings, and texture features. The best diagnostic performance was achieved with a combination of first and second order texture features, clinical information, and subjective radiological findings, indicating the potential of pretreatment DCE-MRI in predicting pCR in breast cancer patients.

MAGNETIC RESONANCE IMAGING (2022)

Article Engineering, Electrical & Electronic

Performance Analysis of XGBoost Ensemble Methods for Survivability with the Classification of Breast Cancer

T. R. Mahesh et al.

Summary: Breast cancer is the most common and rapidly spreading disease globally, and machine learning has been proposed as an effective tool for early detection and diagnosis. The use of Synthetic Minority Oversampling Technique (SMOTE) has been shown to improve classification accuracy, particularly when dealing with imbalanced data issues.

JOURNAL OF SENSORS (2022)

Article Engineering, Multidisciplinary

A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis

Mujeeb Ur Rehman et al.

Summary: This study proposes a novel privacy-preserving non-invasive cancer detection method using Deep Learning. By encrypting clinical data and employing techniques such as Convolutional Neural Network model, high accuracy cancer detection is achieved.

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING (2022)

Article Oncology

Predicting Overall Survival Using Machine Learning Algorithms in Oral Cavity Squamous Cell Carcinoma

Jia Yan Tan et al.

Summary: This study used machine learning algorithms to predict the 3 -and 5-year prognosis of oral cancer patients in Queensland, Australia, and provided clinical interpretability of the predicted outcome made by the ML model.

ANTICANCER RESEARCH (2022)

Article Mathematical & Computational Biology

Prediction of postoperative recovery in patients with acoustic neuroma using machine learning and SMOTE-ENN techniques

Jianing Wang

Summary: This study uses machine learning and SMOTE-ENN techniques to predict the postoperative facial nerve function recovery in patients with acoustic neuroma. It finds that the XGBoost model can accurately predict the recovery and provides important preoperative indicators to improve patients' postoperative outcomes.

MATHEMATICAL BIOSCIENCES AND ENGINEERING (2022)

Review Health Care Sciences & Services

Breast Cancer Dataset, Classification and Detection Using Deep Learning

Muhammad Shahid Iqbal et al.

Summary: Incorporating scientific research into clinical practice through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, improves patient treatment. Computational pathology is a rapidly growing subspecialty that has the potential to integrate whole slide images, multi-omics data, and health informatics for diagnosing breast cancer.

HEALTHCARE (2022)

Article Engineering, Biomedical

Alzheimer's Disease Detection Using Comprehensive Analysis of Timed Up and Go Test via Kinect V.2 Camera and Machine Learning

Mahmoud Seifallahi et al.

Summary: This study investigated the feasibility of using a balance and walking assessment tool to detect Alzheimer's disease (AD) and healthy control (HC). The results showed that using signal processing and statistical analysis, along with a support vector machine classifier, could accurately distinguish between the two groups, demonstrating the potential of this method as a new quantitative tool for detecting AD.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2022)

Article Mathematical & Computational Biology

Prediction of postoperative recovery in patients with acoustic neuroma using machine learning and SMOTE-ENN techniques

Jianing Wang

Summary: This study accurately predicts the postoperative facial nerve function recovery of patients with acoustic neuroma using machine learning and SMOTE-ENN techniques, potentially improving postoperative recovery for patients.

MATHEMATICAL BIOSCIENCES AND ENGINEERING (2022)

Article Computer Science, Artificial Intelligence

Predicting breast cancer survivability based on machine learning and features selection algorithms: a comparative study

Sahar A. El Rahman

Summary: This study aims to identify breast cancer early through machine learning algorithms and feature selection methods. Experimental results indicate that the classification based on RF technique with Genetic Algorithm as a feature selection method in WBC dataset achieved the best accuracy of 96.82%.

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)

Article Engineering, Biomedical

Optimizing Survival Analysis of XGBoost for Ties to Predict Disease Progression of Breast Cancer

Pei Liu et al.

Summary: The EXSA gradient boosting algorithm was developed to predict breast cancer disease progression, achieving competitive performance with a concordance index of 0.83454, 5-year and 10-year AUC of 0.83851 and 0.78155, respectively. This method provides an important means for follow-up data of breast cancer or other disease research.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2021)

Article Biochemical Research Methods

Computational Modeling of Gene-Specific Transcriptional Repression, Activation and Chromatin Interactions in Leukemogenesis by LASSO-Regularized Logistic Regression

Nickolas Steinauer et al.

Summary: Many physiological and pathological pathways rely on gene-specific transcriptional regulation, and the use of LASSO-regularized logistic regression successfully predicted gene-specific transcriptional events and identified rate-limiting factors underlying gene activation and repression differences.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2021)

Article Health Care Sciences & Services

Breast Cancer Type Classification Using Machine Learning

Jiande Wu et al.

Summary: The study proposed a new approach to classify triple negative breast cancer and non-triple negative breast cancer patients using machine learning methods, with the Support Vector Machine algorithm demonstrating higher accuracy and fewer misclassification errors in classifying breast cancer types.

JOURNAL OF PERSONALIZED MEDICINE (2021)

Article Construction & Building Technology

Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls

De-Cheng Feng et al.

Summary: In this study, an advanced machine learning model was trained and interpreted for estimating the shear strengths of squat RC walls, utilizing a database of 434 samples. The model strategically combined the XGBoost algorithm for predictive modeling and the SHAP algorithm for analyzing factor importance. This setup achieved a high level of accuracy in shear strength estimation and provided physical and quantitative interpretations of the input-output dependencies.

JOURNAL OF STRUCTURAL ENGINEERING (2021)

Article Biochemical Research Methods

MGRFE: Multilayer Recursive Feature Elimination Based on an Embedded Genetic Algorithm for Cancer Classification

Cheng Peng et al.

Summary: Gene selection is a challenging task aimed at choosing a minimal number of genes closely associated with a phenotype, and existing feature selection algorithms mostly utilize heuristic rules.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2021)

Article Computer Science, Artificial Intelligence

A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis

Punitha Stephan et al.

Summary: The study presents a hybrid algorithm (HAW) that integrates artificial bee colony and whale optimization algorithms to improve the accuracy of breast cancer diagnosis. The HAW algorithm achieved higher accuracy with lower complexity in breast cancer datasets.

NEURAL COMPUTING & APPLICATIONS (2021)

Article Engineering, Electrical & Electronic

Prediction of Pulmonary Diseases With Electronic Nose Using SVM and XGBoost

V. A. Binson et al.

Summary: This study focuses on using breath samples as biomarkers for the diagnosis of lung cancer, COPD, and asthma, developing an electronic nose system and achieving high diagnostic accuracy through machine learning models.

IEEE SENSORS JOURNAL (2021)

Article Computer Science, Information Systems

A Novel Hybrid K-Means and GMM Machine Learning Model for Breast Cancer Detection

P. Esther Jebarani et al.

Summary: Breast cancer ranks as the second leading cause of death among women worldwide, making early detection and diagnosis vital for treatment. This research presents a new diagnostic technique by integrating segmentation methods and machine learning, aiming to differentiate between benign and malignant tumors effectively.

IEEE ACCESS (2021)

Article Computer Science, Information Systems

A Novel Hybrid Feature Selection and Ensemble Learning Framework for Unbalanced Cancer Data Diagnosis With Transcriptome and Functional Proteomic

Xianfang Tang et al.

Summary: A novel hybrid feature selection and ensemble learning framework, referred to as TSFS-TCEM, is proposed for cancer diagnosis in this study. By combining transcriptome and functional proteomics data to construct multi-omics data, the method achieves high diagnostic accuracy and outperforms existing methods, with sensitivity, specificity, and F-Measure all above 99.63% in 5-fold cross-validation.

IEEE ACCESS (2021)

Article Computer Science, Information Systems

An Enhanced Ensemble Diagnosis of Cervical Cancer: A Pursuit of Machine Intelligence Towards Sustainable Health

Qazi Mudassar Ilyas et al.

Summary: Cervical cancer poses a threat to life, but obstacles like high medical costs hinder disease identification and screening. Machine intelligence offers an affordable and computationally efficient method for early diagnosis, while ensemble classification methods based on majority voting show superior accuracy in diagnosis.

IEEE ACCESS (2021)

Article Computer Science, Artificial Intelligence

A new nested ensemble technique for automated diagnosis of breast cancer

Moloud Abdar et al.

PATTERN RECOGNITION LETTERS (2020)

Article Computer Science, Artificial Intelligence

A novel second-order cone programming support vector machine model for binary data classification

Guishan Dong et al.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS (2020)

Article Computer Science, Interdisciplinary Applications

A novel fitness function in genetic programming for medical data classification

Arvind Kumar et al.

JOURNAL OF BIOMEDICAL INFORMATICS (2020)

Article Computer Science, Information Systems

HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System

Norma Latif Fitriyani et al.

IEEE ACCESS (2020)

Article Computer Science, Information Systems

A Machine Learning Methodology for Diagnosing Chronic Kidney Disease

Jiongming Qin et al.

IEEE ACCESS (2020)

Article Computer Science, Information Systems

Medical Health Big Data Classification Based on KNN Classification Algorithm

Wenchao Xing et al.

IEEE ACCESS (2020)

Article Computer Science, Artificial Intelligence

Multiple instance learning for histopathological breast cancer image classification

P. J. Sudharshan et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)