Relevant bibliographies by topics / Wisconsin breast cancer dataset

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Wisconsin breast cancer dataset'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Wisconsin breast cancer dataset"

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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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Dissertations / Theses on the topic "Wisconsin breast cancer dataset"

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Kolář, Adam. "Koevoluce kartézských genetických algoritmů a neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236080.

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The aim of the thesis is to verify synergy of genetic programming and neural networks. Solution is provided by set of experiments with implemented library built upon benchmark tasks. I've done experiments with directly and also indirectly encoded neural netwrok. I focused on finding robust solutions and the best calculation of configurations, overfitting detection and advanced stimulations of solution with fitness function. Generally better solutions were found using lower values of parameters n_c and n_r. These solutions tended less to be overfitted. I was able to evolve neurocontroller elimi
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Wang, Hongwei. "Effect of Risk and Prognosis Factors on Breast Cancer Survival: Study of a Large Dataset with a Long Term Follow-up." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/math_theses/116.

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The main goal of this study is to seek the effects of some risk and prognostic factors contributing to survival of female invasive breast cancer in United States. The study presents the survival analysis for the adult female invasive breast cancer based on the datasets chosen from the Surveillance Epidemiology and End Results (SEER) program of National Cancer Institute (NCI). In this study, the Cox proportional hazard regression model and logistic regression model were employed for statistical analysis. The odds ratios (OR), hazard ratios (HR) and confidence interval (C.I.) were obtained for t
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SAADIZADEH, SAMAN. "SIGNIFICANTLY ACCURATE SYSTEM FOR BREAST CANCER MALIGNANCY OR BENIGN CLASSIFICATION." Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19429.

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Breast cancer happens to one out of eight females worldwide. It is the most elevated reason for cancer malignancy deadliness among ladies. It is identified by finding the cancerous cells in breast tissue. Novel techniques in medical image processing utilized histopathology dataset images taken by an advanced microscope, and then disintegrate the images by applying various algorithms and techniques. Artificial Intelligence methods are presently being applied for processing pathological imagery and tools. Here in the project work, we concentrate on building up the capability of compu
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Books on the topic "Wisconsin breast cancer dataset"

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Gail, Konop Baker. Cancer Is a Bitch: Or, I'd Rather Be Having a Midlife Crisis. Hachette Books, 2009.

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Book chapters on the topic "Wisconsin breast cancer dataset"

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Jaganathan, P., N. Rajkumar, and R. Nagalakshmi. "A Kernel Based Feature Selection Method Used in the Diagnosis of Wisconsin Breast Cancer Dataset." In Advances in Computing and Communications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22709-7_66.

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Hernández-Julio, Y. F., L. A. Díaz-Pertuz, M. Prieto-Guevara, et al. "Intelligent Fuzzy Clinical Decision Support System to Classify Breast Cancer—Case Study: The Wisconsin Dataset." In Medical Imaging and Computer-Aided Diagnosis. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-16-6775-6_44.

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Modi, Nileshkumar, and Kaushar Ghanchi. "A Comparative Analysis of Feature Selection Methods and Associated Machine Learning Algorithms on Wisconsin Breast Cancer Dataset (WBCD)." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0129-1_23.

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Murphy, Allison. "Breast Cancer Wisconsin (Diagnostic) Data Analysis Using GFS-TSK." In Explainable AI and Other Applications of Fuzzy Techniques. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82099-2_27.

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Haziemeh, Feras A., Saddam Rateb Darawsheh, Muhammad Alshurideh, and Anwar Saud Al-Shaar. "Using Logistic Regression Approach to Predicating Breast Cancer DATASET." In The Effect of Information Technology on Business and Marketing Intelligence Systems. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-12382-5_31.

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Mehdi, Heba, and Furkan Rabee. "Breast Cancer Detection Based on UWB Dataset and Machine Learning." In Machine Learning and Mechanics Based Soft Computing Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6450-3_21.

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Grover, Parul, Anita Choudhary, and Megha Khatri. "Comparison of Different Feature Selection Approaches on Breast Cancer Dataset." In Recent Advances in Metrology. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2468-2_26.

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Aubreville, Marc, Christof A. Bertram, Taryn A. Donovan, Christian Marzahl, Andreas Maier, and Robert Klopfleisch. "Abstract: A Completely Annotated Whole Slide Image Dataset of Canine Breast Cancer to Aid Human Breast Cancer Research." In Bildverarbeitung für die Medizin 2021. Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-33198-6_48.

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Benbrahim, Houssam, Hanaâ Hachimi, and Aouatif Amine. "Comparative Study of Machine Learning Algorithms Using the Breast Cancer Dataset." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36664-3_10.

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Banothu, Nagateja, and M. Prabu. "A Novel Approach for Handling Imbalanced Data in Breast Cancer Dataset." In Pervasive Computing and Social Networking. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2840-6_54.

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Conference papers on the topic "Wisconsin breast cancer dataset"

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Meyyappan, M., Aniket Verma, Ginikunta Sai Karthik Goud, Naga Malleswari T Y J, and S. Ushasukhanya. "Breast Cancer Detection Using Transfer Learning with DCGAN for dataset imbalance." In 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2024. http://dx.doi.org/10.1109/conecct62155.2024.10677058.

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Kurniadi, Felix Indra, and Hanis Amalia Saputri. "Feature Selection Using XGBoost on METABRIC Dataset for Survivability Breast Cancer Detection." In 2024 International Conference on Information Management and Technology (ICIMTech). IEEE, 2024. https://doi.org/10.1109/icimtech63123.2024.10780791.

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Saha, Subrata, Md Motinur Rahman, Md Shamimul Islam, Md Mahmudul Hasan, Adity Bhowmik, and Mohammad Abu Sayid Haque. "Breast Cancer Patient Survival Prediction Using Machine Learning on An Imbalanced Dataset." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11021835.

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Andriani, Anik, Anastasia Meyliana, Indriyanti, Vadlya Maarif, and Akhmad Syukron. "Resampling and Ensemble Method for Enhance Breast Cancer Classification Performance in Imbalance Dataset." In 2024 International Conference on Advanced Information Scientific Development (ICAISD). IEEE, 2024. https://doi.org/10.1109/icaisd63055.2024.10895014.

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Sandhu, Jasjeet Kaur, Chetna Sharma, Amandeep Kaur, Pahul Veer Singh Gogna, and Vrinda Sharma. "Improving Breast Cancer Detection with Deep Learning Techniques: A Study Using CBIS-DDMS Dataset." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10841056.

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D, Hemalatha, N. Gomathi, Alex David S, Almas Begum, and Ruth Naveena N. "Performance Comparison of SVM and Logistic Regression in Breast Cancer Diagnosis Using the WDBC Dataset." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10861978.

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Mansur, Salwa Salsabila, Angelina Prima Kurniati, Eric Rojas, and Adiwijaya. "Process Mining to Improve Clinical Pathways in Breast Cancer Treatment Using the Indonesia Health Insurance Dataset." In 2024 12th International Conference on Information and Communication Technology (ICoICT). IEEE, 2024. http://dx.doi.org/10.1109/icoict61617.2024.10698658.

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Dwivedi, Ankit Kumar, Anjulata Yadav, and R. S. Gamad. "Breast Cancer Disease Prediction using Convolutional Neural Networks with Ultrasound Image Dataset for Healthcare-IOT Application." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11070903.

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Bansal, Aayushi, and Anita Singhrova. "Expression of Concern for: Performance Analysis of Supervised Machine Learning Algorithms for Diabetes and Breast Cancer Dataset." In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE, 2021. http://dx.doi.org/10.1109/icais50930.2021.10702999.

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Ghantasala, G. S. Pradeep, Anjaneyulu Kunchala, Sathiyaraj R, Venkateswarulu Naik B, Yaswanth Raparthi, and P. Vidyullatha. "Machine Learning Based Ensemble Classifier using Wisconsin Dataset For Breast Cancer Prediction." In 2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2023. http://dx.doi.org/10.1109/iciics59993.2023.10421387.

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