ArticleAsian Pacific journal of cancer prevention : APJCP2019
Analysis of Decision Tree and K-Nearest Neighbor Algorithm in the Classification of Breast Cancer.
Article in Asian Pacific journal of cancer prevention : APJCP, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Development and validation of a predictive model for thyroid nodule malignancy risk based on intralesional and perilesional ultrasound radiomic features.Gland surgery · 2026Article
- Deep-learning based adaptive fusion of CC and MLO views for improved mammographic cancer diagnosis.MethodsX · 2026Article
- Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews.Bioengineering (Basel, Switzerland) · 2025Review
- A Deep Learning and Explainable Artificial Intelligence based Scheme for Breast Cancer Detection.Scientific reports · 2025Article
- IL-1β and associated molecules as prognostic biomarkers linked with immune cell infiltration in colorectal cancer: an integrated statistical and machine learning approach.Discover oncology · 2025Article
- A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.Scientific reports · 2025Article
- Colorectal cancer prognosis based on dietary pattern using synthetic minority oversampling technique with K-nearest neighbors approach.Scientific reports · 2024Article
- An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study.Heliyon · 2024Article
- Improved Loss Function for Mass Segmentation in Mammography Images Using Density and Mass Size.Journal of imaging · 2024Article
- Machine learning identification of edible vegetable oils from fatty acid compositions and hyperspectral images.Current research in food science · 2024Article
- Predictive maintenance in Industry 4.0: a survey of planning models and machine learning techniques.PeerJ. Computer science · 2024Article
- Article
- Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer.Asian Pacific journal of cancer prevention : APJCP · 2022Article
- Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings-Preliminary Data and a Pilot Study.Diagnostics (Basel, Switzerland) · 2022Article
- Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods.International journal of environmental research and public health · 2022Article
- Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis.International journal of environmental research and public health · 2022Article
- Review
- Tutorial: Applying Machine Learning in Behavioral Research.Perspectives on behavior science · 2020Article
- Breast Cancer Prediction Based on Multiple Machine Learning Algorithms.Technology in cancer research & treatmentArticle
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2 authors.
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Abstract
objectiveThe death rate of breast tumour is falling as there is progress in its research area. However, it is the most common disease among women. It is a great challenge in designing a machine learning model to evaluate the performance of the classification of breast tumour.
methodsImplementing an efficient classification methodology will support in resolving the complications in analyzing breast cancer. This proposed model employs two machine learning (ML) algorithms for the categorization of breast tumour; Decision Tree and K-Nearest Neighbour (KNN) algorithm is used for the breast tumour classification.
resultThis classification includes the two levels of disease as benign or malignant. These two machine learning algorithms are verified using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset after feature selection using Principal Component Analysis (PCA). The comparison of these two ML algorithms is done using the standard performance metrics.
conclusionThe comparative analysis results indicate that the KNN classifier outperforms the result of the decision-tree classifier in the breast cancer classification.<br />.
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