Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques.
Walid Theib MohammadRonza TeeteHeyam Al-AarajYousef Saleh Yousef RubbaiMajd Mowafaq ArabyatPublished in: Applied bionics and biomechanics (2022)
Breast cancer must be addressed by a multidisciplinary team aiming at the patient's comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.
Keyphrases
- electronic health record
- machine learning
- big data
- deep learning
- public health
- artificial intelligence
- single cell
- palliative care
- rna seq
- cell cycle arrest
- oxidative stress
- cell death
- replacement therapy
- signaling pathway
- cell proliferation
- quality improvement
- pet ct
- combination therapy
- childhood cancer
- smoking cessation