Performance Analysis of Breast Cancer Classification Using Decision Tree Classifiers

Abstract

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 cases of women with cancer are estimated for the year 2016. Early diagnosis of breast cancer is a key factor for long-term survival of cancer patients. Classification plays an important role in breast cancer detection and used by researchers to analyse and classify the medical data. In this research work, priority-based decision tree classifier algorithm has been implemented for Wisconsin Breast cancer dataset. This paper analyzes the different decision tree classifier algorithms for Wisconsin original, diagnostic and prognostic dataset using WEKA software. The performance of the classifiers are evaluated against the parameters like accuracy,

Frequency and Distribution of Cytoplasmic and Nuclear P63 in Recognizing the Basal-Like and Non-Basal-Like Types of Breast Cancer Based on Age.

“…However, in the non-basal-like type, a cytoplasmic p63 protein was lower than nuclear p63 protein. The p63 protein is a marker of squamous cell carcinoma at subcellular normal cells or hyperplastic ones appearing significantly in the epithelial cells and contributing to the increased invasion and aggressiveness of the tumor [26]. Specifically, the results of this research indicated that the sensitivity and specificity of cytoplasmic p63 protein in metaplastic breast cancer with basal-like type was 88.2%, while the sensitivity and specificity of nuclear p63 protein was 11.8%.…”

“…The basal-like and TNBC were associated [23]. In addition, the expression of p63 protein derived from basal epithelial cells is strongly influenced by the intensity of tumor cell development such as cell nutrition factor [24], chemotherapy, regulation, and p63 protein synthesis [25] both in nucleus and cytoplasm [26].…”

“…Pregnancy is often associated with breast cancer, generating mortality risk that conducting prognosis is one of the efforts to assess the development of breast cancer [25,26]. One of the techniques used to conduct the prognosis is using a marker to detect immunohistochemically.…”

A Research on Computer Aided Detection System for Women Breast Cancer Diagnosis from Digital Mammographic Images

“…Thus the computer aided diagnosis demands an enhancement in mammographic images there by increasing the efficiency in detection based on advanced image processing and pattern recognition techniques. P. Hamsagayathri, P. Sampath [5],proposed a work to analyzes the different decision tree classifier algorithms for Wisconsin original, diagnostic and prognostic dataset using WEKA software. The performance of the classifiers are evaluated against the parameters like accuracy, Kappa statistic, Entropy, RMSE, TP Rate, FP Rate, Precision, Recall, F-Measure, ROC, Specificity, Sensitivity.…”

Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy

“…Random Trees have been introduced by Leo Breiman and Adele Cutler. This algorithm can deal with both classification and regression problems [21,35]. The structure for a Random Tree is shown in Fig.…”