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Journal articles on the topic 'Wisconsin breast cancer dataset'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

N, Saranya, and Kavi Priya S. "Diagnosis of breast cancer using machine learning algorithms based on features selected by Genetic Algorithm: Assessed on five datasets." Journal of University of Shanghai for Science and Technology 23, no. 11 (2021): 749–58. http://dx.doi.org/10.51201/jusst/21/11963.

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Breast Cancer is one of the chronic diseases occurred to human beings throughout the world. Early detection of this disease is the most promising way to improve patients’ chances of survival. The strategy employed in this paper is to select the best features from various breast cancer datasets using a genetic algorithm and machine learning algorithm is applied to predict the outcomes. Two machine learning algorithms such as Support Vector Machines and Decision Tree are used along with Genetic Algorithm. The proposed work is experimented on five datasets such as Wisconsin Breast Cancer-Diagnosi

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Kurugh, Kumawuese Jennifer, Muhammad Aminu Ahmad, and Awwal Ahmad Babajo. "THE EFFECT OF DATASETS ON BREAST CANCER DETECTION MODELS." FUDMA JOURNAL OF SCIENCES 4, no. 4 (2021): 309–15. http://dx.doi.org/10.33003/fjs-2020-0404-487.

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Datasets are a major requirement in the development of breast cancer classification/detection models using machine learning algorithms. These models can provide an effective, accurate and less expensive diagnosis method and reduce life losses. However, using the same machine learning algorithms on different datasets yields different results. This research developed several machine learning models for breast cancer classification/detection using Random forest, support vector machine, K Nearest Neighbors, Gaussian Naïve Bayes, Perceptron and Logistic regression. Three widely used test data sets

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Chakravarty, Alok, and Shweta Tewari. "Detecting Breast Cancer Using Visual ML." Journal of Neonatal Surgery 14, no. 4S (2025): 1211–16. https://doi.org/10.52783/jns.v14.1933.

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Approximately 60% of Breast cancer patients are diagnosed in advanced stages. This paper examines the automation of identification of cancerous cells using visual machine learning approach. Results are obtained using two different datasets: Wisconsin and Coimbra. In Wisconsin dataset, predictors are extracted from the digitised image of a fine needle aspirate (FNA) of a breast mass. In Coimbra dataset, predictors are extracted from the blood analysis. Ten machine learning models are compared using a visual ML tool called Orange. Particular emphasis is placed on the metric “recall”. Recall is d

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Awan, Muhammad Zeerak, Muhammad Shoaib Arif, Mirza Zain Ul Abideen, and Kamaleldin Abodayeh. "Comparative analysis of machine learning models for breast cancer prediction and diagnosis: a dual-dataset approach." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (2024): 2032. http://dx.doi.org/10.11591/ijeecs.v34.i3.pp2032-2044.

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<p>Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast

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Awan, Muhammad Zeerak, Muhammad Shoaib Arif, Mirza Zain Ul Abideen, and Kamaleldin Abodayeh. "Comparative analysis of machine learning models for breast cancer prediction and diagnosis: a dual-dataset approach." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (2024): 2032–44. https://doi.org/10.11591/ijeecs.v34.i3.pp2032-2044.

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Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast can

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Azis, Azminuddin I. S., Irma Surya Kumala Idris, Budy Santoso, and Yasin Aril Mustofa. "Pendekatan Machine Learning yang Efisien untuk Prediksi Kanker Payudara." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 3, no. 3 (2019): 458–69. http://dx.doi.org/10.29207/resti.v3i3.1347.

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Breast Cancer is the most common cancer found in women and the death rate is still in second place among other cancers. The high accuracy of the machine learning approach that has been proposed by related studies is often achieved. However, without efficient pre-processing, the model of Breast Cancer prediction that was proposed is still in question. Therefore, this research objective to improve the accuracy of machine learning methods through pre-processing: Missing Value Replacement, Data Transformation, Smoothing Noisy Data, Feature Selection / Attribute Weighting, Data Validation, and Unba

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Aamir, Sanam, Aqsa Rahim, Zain Aamir, et al. "Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques." Computational and Mathematical Methods in Medicine 2022 (August 16, 2022): 1–13. http://dx.doi.org/10.1155/2022/5869529.

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Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playin

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Oyelakin, Akinyemi Moruff. "A Model for the Classification of Breast Cancer Using Random Forest Algorithm." DIU Journal of Science & Technology 16, no. 2 (2024): 1–5. https://doi.org/10.5281/zenodo.13827503.

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Breast cancer is a common disease among women globally. Past studies have used Machine learning techniques to speed up the prediction of the disease using labeled datasets. This study proposed a supervised machine learning approach for the classification of breast cancer. The model was built using Random Forest Algorithm. The dataset chosen for this study is a Wisconsin breast cancer (Diagnostic) dataset. The breast cancer dataset was originally released by the University of Wisconsin Hospitals, Madison. Python programming language and some of its libraries were used for the experimental analy

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Das, Sumit, Subhodip Koley, and Tanusree Saha. "Machine Learning Approaches for Investigating Breast Cancer." Biosciences Biotechnology Research Asia 20, no. 4 (2023): 1109–31. http://dx.doi.org/10.13005/bbra/3163.

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ABSTRACT: This study aims to predict whether the case is malignant or benign and concentrate on the anticipated diagnosis; if the case is malignant, it is advised to admit the patient to the hospital for treatment. The primary goal of this work is to put together models in two distinct datasets to predict breast cancer more accurately, faster, and with fewer errors than before. Then contrast the techniques that produced datasets with the highest accuracy. In this study, the datasets were processed using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbours, Artificia

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reddy, Anuradha. "Support Vector Machine Classifier For Prediction Of Breast Malignancy Using Wisconsin Breast Cancer Dataset." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 21 (January 1, 2022): 1–8. http://dx.doi.org/10.55529/jaimlnn.21.1.8.

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Cancer is the world's second largest cause of death. In 2018, 9.6 million people died from cancer. In any medical sickness, breast cancer is one of the most delicate and endemic diseases. This is one of the primary causes of female death in the world. Breast cancer kills one out of every eleven women around the world. "Early detection equals improved odds of survival," says a well-known cancer adage. As a result, early detection is essential for successfully preventing breast cancer and lowering morality. Breast Cancer is a type of cancer that affects one of the most significant issues that hu

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reddy, Anuradha. "Support Vector Machine Classifier For Prediction Of Breast Malignancy Using Wisconsin Breast Cancer Dataset." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 21 (January 1, 2022): 1–8. http://dx.doi.org/10.55529/jaimlnn21.1.8.

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Cancer is the world's second largest cause of death. In 2018, 9.6 million people died from cancer. In any medical sickness, breast cancer is one of the most delicate and endemic diseases. This is one of the primary causes of female death in the world. Breast cancer kills one out of every eleven women around the world. "Early detection equals improved odds of survival," says a well-known cancer adage. As a result, early detection is essential for successfully preventing breast cancer and lowering morality. Breast Cancer is a type of cancer that affects one of the most significant issues that hu

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Kandhasamy, Premalatha, Duraisamy Prabha Devi, and Sivakumar Kandhasamy. "Machine learning framework for breast cancer detection with feature selection with L2 ridge regularization: insights from multiple datasets." Journal of Translational Genetics and Genomics 9, no. 1 (2025): 11–34. https://doi.org/10.20517/jtgg.2024.82.

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Aim: This study aims to investigate and apply effective machine learning techniques for the early detection and precise diagnosis of breast cancer. The analysis is conducted using various breast cancer datasets, including Breast Cancer Wisconsin, Breast Cancer Diagnosis, NKI Breast Cancer, and SEER Breast Cancer datasets. The primary focus is on identifying key features and utilizing preprocessing methods to enhance classification accuracy. Methods: The datasets undergo several preprocessing steps, such as label encoding for categorical variables, linear regression for handling missing values,

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Manir, Shamiha Binta, and Priya Deshpande. "Critical Risk Assessment, Diagnosis, and Survival Analysis of Breast Cancer." Diagnostics 14, no. 10 (2024): 984. http://dx.doi.org/10.3390/diagnostics14100984.

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Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epi

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Kadhim, Rania R., and Mohammed Y. Kamil. "Comparison of breast cancer classification models on Wisconsin dataset." International Journal of Reconfigurable and Embedded Systems (IJRES) 11, no. 2 (2022): 166. http://dx.doi.org/10.11591/ijres.v11.i2.pp166-174.

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Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numer

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Rania, R. Kadhim, and Y. Kamil Mohammed. "Comparison of breast cancer classification models on Wisconsin dataset." International Journal of Reconfigurable and Embedded Systems (IJRES) 11, no. 2 (2022): 166–74. https://doi.org/10.11591/ijres.v11.i2.pp166-174.

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Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy n

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Tian, Jianxue, Jue Zhang, Xiaofen Tang, and Ting Dong. "A Hybrid of Random Over Sample Examples and Boosted C5.0 Algorithms for Breast Cancer Diagnosis on Imbalanced Data." Journal of Medical Imaging and Health Informatics 10, no. 11 (2020): 2686–92. http://dx.doi.org/10.1166/jmihi.2020.3201.

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To surmount the two-class imbalanced problem existing in the breast cancer diagnosis, a hybrid method of ROSE sampling approach with Boosted C5.0 ensemble classifier (R-Boosted C5.0) is proposed. ROSE as the sampling method is utilized to balance the class distribution. Boosted C5.0 is then used as the classifier. To serve this purpose, Wisconsin Breast Cancer Dataset (WBCD), Wisconsin Diagnosis Breast Cancer (WDBC) and three imbalanced datasets have been studied. Assessing by Matthews Correlation Coefficient (MCC), the performance of proposed method on WBCD and WDBC datasets were 98.5% and 93

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Peng, Lifang, Bin Huang, Kefu Chen, and Leyuan Zhou. "A Novel Breast Cancer Detection Technology Using an Advanced Transfer Maximal Entropy Clustering Algorithm." Journal of Medical Imaging and Health Informatics 9, no. 8 (2019): 1639–44. http://dx.doi.org/10.1166/jmihi.2019.2775.

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The initial diagnosis of breast cancer involves analyzing the relevant examination report of the patient to determine whether the tumor is benign or malignant. Unsupervised clustering algorithms can be used with this type of problem. In a cluster analysis of a patient's examination data, the clustering results and the preliminary diagnosis results are obtained. However, due to the high cost of detection, medical datasets often have a small sample size or lack information. The traditional clustering technique usually has poor clustering effects in such scenarios. To solve this problem, this pap

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Prastyo, Pulung Hendro, I. Gede Yudi Paramartha, Michael S. Moses Pakpahan, and Igi Ardiyanto. "Predicting Breast Cancer: A Comparative Analysis of Machine Learning Algorithms." Proceeding International Conference on Science and Engineering 3 (April 30, 2020): 455–59. http://dx.doi.org/10.14421/icse.v3.545.

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Breast cancer is the most common cancer among women (43.3 incidents per 100.000 women), with the highest mortality (14.3 incidents per 100.000 women). Early detection is critical for survival. Using machine learning approaches, the problem can be effectively classified, predicted, and analyzed. In this study, we compared eight machine learning algorithms: Gaussian Naïve Bayes (GNB), k-Nearest Neighbors (K-NN), Support Vector Machine(SVM), Random Forest (RF), AdaBoost, Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). The experiment is conducted using Breast Cancer Wisconsin da

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Akkur, Erkan, Fuat Türk, and Osman Erogul. "Breast cancer classification using a novel hybrid feature selection approach." Neural Network World 33, no. 2 (2023): 67–83. http://dx.doi.org/10.14311/nnw.2023.33.005.

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Many women around the world die due to breast cancer. If breast cancer is treated in the early phase, mortality rates may significantly be reduced. Quite a number of approaches have been proposed to help in the early detection of breast cancer. A novel hybrid feature selection model is suggested in this study. This novel hybrid model aims to build an efficient feature selection method and successfully classify breast lesions. A combination of relief and binary Harris hawk optimization (BHHO) hybrid model is used for feature selection. Then, k-nearest neighbor (k-NN), support vector machine (SV

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Jain, Bhoomi, and Neetu Singla. "Breast Cancer Detection using Machine Learning Algorithms." Journal of Computers, Mechanical and Management 2, no. 6 (2023): 30–35. http://dx.doi.org/10.57159/gadl.jcmm.2.6.230109.

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Machine learning employs classification methods on datasets. The Machine Learning repository provided the cancer datasets that were used in this study, which were used for categorization. Breast cancer databases come in two varieties. There are various numbers of characteristics dispersed among these datasets. Breast cancer observes around 14\% of all female cancers. One in every 28 women will develop breast cancer. To analyse patterns in datasets, machine learning algorithms like SVM, KNN, and decision trees are used. Computers are able to ``learn'' from their past mistakes and come up with s

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Ellingsen, Herman, Aliaksandr Hubin, Filippo Remonato, and Solve Sæbø. "Outlier Detection in Bayesian Neural Networks." Nordic Machine Intelligence 4, no. 1 (2024): 1–15. http://dx.doi.org/10.5617/nmi.11406.

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Describing uncertainty is one of the major issues in modern deep learning. Artificial Intelligence models could be used with greater confidence by having solid methods for identifying and quantifying uncertainty. This article proposes two alternative methods for outlier detection in Bayesian Neural Networks used for classification tasks. This is done by looking for unusual pre-activation neuron values in the last layer of a Bayesian Neural Network. The proposed methods are compared to a baseline method for outlier detection, Predictive Entropy, on three datasets: a simulated dataset, the MNIST

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Hamsagayathri, P., and P. Sampath. "PERFORMANCE ANALYSIS OF BREAST CANCER CLASSIFICATION USING DECISION TREE CLASSIFIERS." International Journal of Current Pharmaceutical Research 9, no. 2 (2017): 19. http://dx.doi.org/10.22159/ijcpr.2017v9i2.17383.

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Breast cancer is one of the dangerous cancers among world’s women above 35 y. The breast is made up of lobules that secrete milk and thin milk ducts to carry milk from lobules to the nipple. Breast cancer mostly occurs either in lobules or in milk ducts. The most common type of breast cancer is ductal carcinoma where it starts from ducts and spreads across the lobules and surrounding tissues. According to the medical survey, each year there are about 125.0 per 100,000 new cases of breast cancer are diagnosed and 21.5 per 100,000 women due to this disease in the United States. Also, 246,660 new

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Rahmanul Hoque, Suman Das, Mahmudul Hoque, and Mahmudul Hoque. "Breast Cancer Classification using XGBoost." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 1985–94. http://dx.doi.org/10.30574/wjarr.2024.21.2.0625.

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Breast cancer continues to be one of the foremost illnesses that results in the deaths of numerous women each year. Among the female population, approximately 8% are diagnosed with Breast cancer (BC), following Lung Cancer. The alarming rise in fatality rates can be attributed to breast cancer being the second leading cause. Breast cancer manifests through genetic transformations, persistent pain, alterations in size, color (redness), and texture of the breast's skin. Pathologists rely on the classification of breast cancer to identify a specific and targeted prognosis, achieved through binary

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Rahmanul, Hoque, Das Suman, Hoque Mahmudul, and Haque Ehteshamul. "Breast Cancer Classification using XGBoost." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 1985–94. https://doi.org/10.5281/zenodo.14043719.

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Breast cancer continues to be one of the foremost illnesses that results in the deaths of numerous women each year. Among the female population, approximately 8% are diagnosed with Breast cancer (BC), following Lung Cancer. The alarming rise in fatality rates can be attributed to breast cancer being the second leading cause. Breast cancer manifests through genetic transformations, persistent pain, alterations in size, color (redness), and texture of the breast's skin. Pathologists rely on the classification of breast cancer to identify a specific and targeted prognosis, achieved through binary

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Suresh, Tamilarasi, Assegie Tsehay Admassu, Sangeetha Ganesan, Tulasi Ravulapalli Lakshmi, Radha Mothukuri, and Salau Ayodeji Olalekan. "Explainable extreme boosting model for breast cancer diagnosis." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5764–69. https://doi.org/10.11591/ijece.v13i5.pp5764-5769.

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This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains t

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Singh, Shatakshi, Sunil Kumar Jangir, Manish Kumar, et al. "Feature Importance Score-Based Functional Link Artificial Neural Networks for Breast Cancer Classification." BioMed Research International 2022 (April 2, 2022): 1–8. http://dx.doi.org/10.1155/2022/2696916.

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Growth of malignant tumors in the breast results in breast cancer. It is a cause of death of many women across the world. As a part of treatment, a woman might have to go through painful surgery and chemotherapy that may further lead to severe side effects. However, it is possible to cure it if it is diagnosed in the initial stage. Recently, many researchers have leveraged machine learning (ML) techniques to classify breast cancer. However, these methods are computationally expensive and prone to the overfitting problem. A simple single-layer neural network, i.e., functional link artificial ne

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Jain, Parul, Shalini Aggarwal, Sufiyan Adam, and Mohsin Imam. "Parametric optimization and comparative study of machine learning and deep learning algorithms for breast cancer diagnosis." Breast Disease 43, no. 1 (2024): 257–70. http://dx.doi.org/10.3233/bd-240018.

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Breast Cancer is the leading form of cancer found in women and a major cause of increased mortality rates among them. However, manual diagnosis of the disease is time-consuming and often limited by the availability of screening systems. Thus, there is a pressing need for an automatic diagnosis system that can quickly detect cancer in its early stages. Data mining and machine learning techniques have emerged as valuable tools in developing such a system. In this study we investigated the performance of several machine learning models on the Wisconsin Breast Cancer (original) dataset with a part

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Chandhare, Pranita, Harshwardhan Gaikwad, and Prashant Bangar. "Breast Cancer Prediction Using Logistic Regression." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1630–35. https://doi.org/10.22214/ijraset.2025.67963.

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Abstract: In this paper, we research the early detection of breast cancer. We use a machine learning approach. We use the Breast Cancer Wisconsin Diagnostic dataset (WBCD). The major focus is on the predictive model of logistic regression. Firstly, we check performance metrics: accuracy, precision-recall, and F1 score. We achieved a good accuracy, which helps to prove the model is acceptable, as it is simple and interpretable. The results show that logistic regression is an effective method for detecting cancers, making it a promising choice for breast cancer diagnosis. The study also examines

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Hernández-Julio, Yamid Fabián, Leonardo Antonio Díaz-Pertuz, Martha Janeth Prieto-Guevara, Mauricio Andrés Barrios-Barrios, and Wilson Nieto-Bernal. "Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset." International Journal of Environmental Research and Public Health 20, no. 6 (2023): 5103. http://dx.doi.org/10.3390/ijerph20065103.

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Decision Support Systems (DSSs) are solutions that serve decision-makers in their decision-making process. For the development of these intelligent systems, two primary components are needed: the knowledge database and the knowledge rule base. The objective of this research work was to implement and validate diverse clinical decision support systems supported by Mamdani-type fuzzy set theory using clustering and dynamic tables. The outcomes were evaluated with other works obtained from the literature to validate the suggested fuzzy systems for categorizing the Wisconsin breast cancer dataset.

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Kumari, Madhu, and Prachi Ahlawat. "Intelligent Information Retrieval for Reducing Missed Cancer and Improving the Healthcare System." International Journal of Information Retrieval Research 12, no. 1 (2022): 1–25. http://dx.doi.org/10.4018/ijirr.2022010102.

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This study presents an intelligent information retrieval system that will effectively extract useful information from breast cancer datasets and utilized that information to build a classification model. The proposed model will reduce the missed cancer rate by providing a comprehensive decision support to the radiologist. The model is built on two datasets, Wisconsin Breast Cancer Dataset (WBCD) and 365 free text mammography reports from a hospital. Effective pre-processing techniques including filling missing values with regression, an effective Natural Language Processing (NLP) Parser is dev

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Subasree, S., N. K. Sakthivel, M. Shobana, and Amit Kumar Tyagi. "Deep Learning based Improved Generative Adversarial Network for Addressing Class Imbalance Classification Problem in Breast Cancer Dataset." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 31, no. 03 (2023): 387–412. http://dx.doi.org/10.1142/s0218488523500204.

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The breast cancer diagnosis is one of the challenging tasks of medical field. Especially, the breast cancer diagnosis among younger women (under 40 years old) is more complicated, because their breast tissue is generally denser than the older women. The Breast Cancer Wisconsin image dataset contains two classes: (i) Benign (Minority class), (ii) Malignant (Majority class). The imbalanced class distribution leads to a deterioration in the classifier model performance owing to the biased classification towards the majority class. Therefore, in this article, an improved generative adversarial net

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Bekkouche, Amina, Mohammed Merzoug, Mourad Hadjila, and Wafaa Ferhi. "Towards Early Breast Cancer Detection: A Deep Learning Approach." Engineering, Technology & Applied Science Research 14, no. 5 (2024): 17517–23. http://dx.doi.org/10.48084/etasr.8634.

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Early detection of breast cancer is crucial for patients' recovery chances to be improved. Artificial intelligence techniques, and more particularly Deep Learning (DL), may contribute to enhancing the accuracy of this detection. The main objective of this paper is to propose a DL model in an attempt to detect and classify breast cancer, and thus help people suffering from this disease. The Breast Cancer Wisconsin dataset was implemented to train neural networks, and their performance was subsequently evaluated on certain test datasets. The findings revealed that this approach provides promisin

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Gurcan, Fatih, and Ahmet Soylu. "Learning from Imbalanced Data: Integration of Advanced Resampling Techniques and Machine Learning Models for Enhanced Cancer Diagnosis and Prognosis." Cancers 16, no. 19 (2024): 3417. http://dx.doi.org/10.3390/cancers16193417.

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Background/Objectives: This study aims to evaluate the performance of various classification algorithms and resampling methods across multiple diagnostic and prognostic cancer datasets, addressing the challenges of class imbalance. Methods: A total of five datasets were analyzed, including three diagnostic datasets (Wisconsin Breast Cancer Database, Cancer Prediction Dataset, Lung Cancer Detection Dataset) and two prognostic datasets (Seer Breast Cancer Dataset, Differentiated Thyroid Cancer Recurrence Dataset). Nineteen resampling methods from three categories were employed, and ten classifie

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Vivekanandan, S., S. Mounika, P. Monisha, and M. Balaganesh. "Robust Breast Cancer Prognosis Prediction: Adaptive Outlier Removal using SVM and K-Means Clustering." March 2024 6, no. 1 (2024): 85–99. http://dx.doi.org/10.36548/jscp.2024.1.007.

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Analyzing several datasets is essential to breast cancer research in order to find trends and prognostic markers. For this reason, the Wisconsin Prognostic Breast Cancer (WPBC) dataset offers a valuable source of data. Outliers, however, have the potential to seriously affect how accurate predictive models are. This work suggests using the Support Vector Machine (SVM) algorithm in an adaptive outlier removal method to improve the resilience of prediction models that were trained on the WPBC dataset. To ensure optimum SVM performance, the technique includes pre-processing processes, including a

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Saheb karan, Abhik Roy Chowdhury, Amit Pal, Susmita Das, Sulekha Das, and Avijit Kumar Chaudhuri. "Early Detection Of Breast Cancer Using Logistic Regression Method." international journal of engineering technology and management sciences 7, no. 2 (2023): 133–42. http://dx.doi.org/10.46647/ijetms.2023.v07i02.017.

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Breast cancer is the most frequently occurring cancer disease in women. It is reported almost 14% of cancers in Indian women are breast cancer. It becomes very crucial to predict breast cancer earlier to minimize the deaths. This research article helps to predict breast cancer earlier and reduce the immature deaths of women in India. In this paper, the authors have used the Logistic Regression method to classify the disease. The authors simulate the results using logistic regression with 10-fold cross-validations and with a different train-test split of the dataset. The 10-fold cross validatio

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Egwom, Onyinyechi Jessica, Mohammed Hassan, Jesse Jeremiah Tanimu, Mohammed Hamada, and Oko Michael Ogar. "An LDA–SVM Machine Learning Model for Breast Cancer Classification." BioMedInformatics 2, no. 3 (2022): 345–58. http://dx.doi.org/10.3390/biomedinformatics2030022.

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Breast cancer is a prevalent disease that affects mostly women, and early diagnosis will expedite the treatment of this ailment. Recently, machine learning (ML) techniques have been employed in biomedical and informatics to help fight breast cancer. Extracting information from data to support the clinical diagnosis of breast cancer is a tedious and time-consuming task. The use of machine learning and feature extraction techniques has significantly changed the whole process of a breast cancer diagnosis. This research work proposed a machine learning model for the classification of breast cancer

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Alsabry, Ayman, and Malek Algabri. "Iterative Tuning of Tree-Ensemble-Based Models' parameters Using Bayesian Optimization for Breast Cancer Prediction." Informatics and Automation 23, no. 1 (2024): 129–68. http://dx.doi.org/10.15622/ia.23.1.5.

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The study presents a method for iterative parameter tuning of tree ensemble-based models using Bayesian hyperparameter tuning for states prediction, using breast cancer as an example. The proposed method utilizes three different datasets, including the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the Surveillance, Epidemiology, and End Results (SEER) breast cancer dataset, and the Breast Cancer Coimbra dataset (BCCD), and implements tree ensemble-based models, specifically AdaBoost, Gentle-Boost, LogitBoost, Bag, and RUSBoost, for breast cancer prediction. Bayesian optimization was used

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Rasool, Abdur, Chayut Bunterngchit, Luo Tiejian, Md Ruhul Islam, Qiang Qu, and Qingshan Jiang. "Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis." International Journal of Environmental Research and Public Health 19, no. 6 (2022): 3211. http://dx.doi.org/10.3390/ijerph19063211.

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Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the ro

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Kusuma, Edi, Guruh Shidik, and Ricardus Pramunendar. "Optimization of Neural Network using Nelder Mead in Breast Cancer Classification." International Journal of Intelligent Engineering and Systems 13, no. 6 (2020): 330–37. http://dx.doi.org/10.22266/ijies2020.1231.29.

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Classification is one of the data mining techniques which considered as supervised learning. Classification technique such as Backpropagation Neural Network (BPNN) has been utilized in several fields to increase human productivity. BPNN can give better results (more natural) compared with other statistical techniques. However, the learning process of BPNN could give an inefficient synapse weight of each hidden layer. This ineffective weight can affect the performance of the network. In this research, BPNN optimization using Nelder Mead to identifying the appearance of breast cancer is proposed

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Moldovanu, Simona, Iulia-Nela Anghelache Nastase, Mihaela Miron, and Luminita Moraru. "Performance comparison of two non-parametric classifiers for classification using geometric features." Annals of the ”Dunarea de Jos” University of Galati Fascicle II Mathematics Physics Theoretical Mechanics 45, no. 2 (2022): 59–62. http://dx.doi.org/10.35219/ann-ugal-math-phys-mec.2022.2.04.

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This study aims to examine and compare the performances of Random Forest (RF) and k-Nearest Neighbor (k-NN) algorithms used for classification based on certain geometric features. For the purpose of the analysis, the Breast Cancer Wisconsin (BCW) public dataset is used. BCW dataset contains features like area, perimeter, radius, compactness, and symmetry computed from 357 benign, and 212 malignant breast images, respectively. Three different experiments related to the size of training and testing datasets for classification are conducted and different accuracy values are obtained. The best acc

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Chuiko, Gennady, and Denys Honcharov. "Dimensionality cutback and deep learning algorithms efficacy as to the breast cancer diagnostic dataset." Radioelectronic and Computer Systems 2024, no. 4 (2024): 91–98. https://doi.org/10.32620/reks.2024.4.08.

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Breast cancer is a significant threat because it is the most frequently diagnosed form of cancer and one of the leading causes of mortality among women. Early diagnosis and timely treatment are crucial for saving lives and reducing treatment costs. Various medical imaging techniques, such as mammography, computed tomography, histopathology, and ultrasound, are contemporary approaches for detecting and classifying breast cancer. Machine learning professionals prefer Deep Learning algorithms when analyzing substantial medical imaging data. However, the application of deep learning-based diagnost

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Abdurrahman, Ginanjar. "Klasifikasi Kanker Payudara Menggunakan Algoritma SVM dengan Kernel RBF, Linier, dan Sigmoid." JUSTIFY : Jurnal Sistem Informasi Ibrahimy 2, no. 1 (2023): 74–80. http://dx.doi.org/10.35316/justify.v2i1.3370.

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Breast cancer ranks first in both the gender category and the death rate. Late treatment is often found in cases of breast cancer which causes an increase in the risk factors for this cancer. For this reason, early detection of breast cancer is needed, so that treatment can be done in a timely manner, so that the death rate due to breast cancer can be reduced. For this reason, this article offers early detection of breast cancer using classification. The dataset in this study used the Wisconsin breast cancer dataset taken from Kaggle. Initially the dataset has a missing value, besides that the

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Vig, Leena. "Comparative Analysis of Different Classifiers for the Wisconsin Breast Cancer Dataset." OALib 01, no. 06 (2014): 1–7. http://dx.doi.org/10.4236/oalib.1100660.

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Sukmandhani, Arief Agus, Lukas, Yaya Heryadi, Wayan Suparta, and Antoni Wibowo. "Classification Algorithm Analysis for Breast Cancer." E3S Web of Conferences 388 (2023): 02012. http://dx.doi.org/10.1051/e3sconf/202338802012.

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Breast cancer in women is a type of disease that is the main cause of death in women according to world breast cancer data. Therefore, early detection of breasts is needed significantly to improve life. If a woman has been identified, then rehabilitation and treatment on an incentive basis are needed to reduce the worse. This study used a dataset collected by the University of Wisconsin Hospitals, Madison (https://atapdata.ai/). This research conducted experiments using several data mining classification strategies to predict breast cancer using machine learning algorithms. The Support Vector

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I, Arathi Chandran R., and V. Mary Amala Bai. "Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence." Journal of Applied Engineering and Technological Science (JAETS) 5, no. 1 (2023): 495–514. http://dx.doi.org/10.37385/jaets.v5i1.3384.

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With more than 2.1 million new cases of diagnosis each year, breast cancer is considered to be the most prevalent women disease. Within 10 years, nearly 30% patients who got cured at early-stages experienced cancer recurrence. Recurrence is a crucial aspect of breast cancer behaviour that is inseparably linked to mortality. Despite its importance, the significant proportion of breast cancer datasets rarely include it, which makes research into its prediction more challenging. It is still difficult to predict who will experience a recurrence and who won't, which has implications for the treatme

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Morkonda Gunasekaran, Dinesh, and Prabha Dhandayudam. "Design of novel multi filter union feature selection framework for breast cancer dataset." Concurrent Engineering 29, no. 3 (2021): 285–90. http://dx.doi.org/10.1177/1063293x211016046.

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Nowadays women are commonly diagnosed with breast cancer. Feature based Selection method plays an important step while constructing a classification based framework. We have proposed Multi filter union (MFU) feature selection method for breast cancer data set. The feature selection process based on random forest algorithm and Logistic regression (LG) algorithm based union model is used for selecting important features in the dataset. The performance of the data analysis is evaluated using optimal features subset from selected dataset. The experiments are computed with data set of Wisconsin dia

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Cakmak, Yigitcan, and Ishak Pacal. "Enhancing Breast Cancer Diagnosis: A Comparative Evaluation of Machine Learning Algorithms Using the Wisconsin Dataset." Journal of Operations Intelligence 3, no. 1 (2025): 175–96. https://doi.org/10.31181/jopi31202539.

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Breast cancer remains a leading cause of morbidity, particularly among women, underscoring the critical importance of early detection. In recent years, highly accurate machine learning algorithms have revolutionized breast cancer identification, significantly improving early diagnosis by analyzing tumor attributes to aid in detection and treatment decisions. This study evaluates seven machine learning algorithms using the Wisconsin breast cancer dataset, revealing that the Support Vector Machines (SVM) algorithm outperforms all others with an exceptional accuracy of 97.66%. These findings high

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Mohd Nasir, Haslinah, Noor Mohd Ariff Brahin, Suraya Zainuddin, Mohd Syafiq Mispan, Ida Syafiza Md Isa, and Mohd Nurul Al Hafiz Sha’abani. "The Comparative Study of Deep Learning Neural Network Approaches for Breast Cancer Diagnosis." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 06 (2023): 127–40. http://dx.doi.org/10.3991/ijoe.v19i06.34905.

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Breast cancer is one of the life threatening cancer that leads to the most death due to cancer among the women. Early diagnosis might help to reduce mortality. Thus, this research aims to study on different approaches of the deep learning neural network model for breast cancer early detection for better prognosis. The performance of deep learning approaches such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) are evaluated using the dataset from the University of Wisconsin. The findings show ANN achieved high accuracy of 99.9 % compared t

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Jakkaladiki, Sudha Prathyusha, and Filip Maly. "Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis." PeerJ Computer Science 10 (February 28, 2024): e1850. http://dx.doi.org/10.7717/peerj-cs.1850.

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Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging techno

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Wan, Nor Liyana Wan Hassan Ibeni, Zaki Mohd Salikon Mohd, Mustapha Aida, Adli Daud Saiful, and Najib Mohd Salleh Mohd. "Comparative analysis on bayesian classification for breast cancer problem." Bulletin of Electrical Engineering and Informatics 8, no. 4 (2019): 1303–11. https://doi.org/10.11591/eei.v8i4.1628.

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The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian network

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