Breast Cancer Data Analytics With Missing Values: A study on Ethnic, Age and Income Groups
An analysis of breast cancer incidences in women and the relationship between ethnicity and survival rate has been an ongoing study with recorded incidences of missing values in the secondary data. In this paper, we study and report the results of breast cancer survival rate by ethnicity, age and income groups from the dataset collected for 53593 patients in South East England between the years 1998 and 2003. In addition to this, we also predict the missing values for the ethnic groups in the dataset. The principle findings in our study suggest that: 1) women of white ethnicity in South East England have a highest percentage of survival rate when compared to the black ethnicity, 2) High income groups have higher survival rates to that of lower income groups and 3) Age groups between 80-95 have lower percentage of survival rate.
Code (0)
등록된 구현이 없습니다.
Tasks
Missing ValuesSimilar Papers 제목 키워드 기반
Imputation techniques on missing values in breast cancer treatment and fertility data
Clinical decision support using data mining techniques offers more intelligent way to reduce the decision error in the last few years. However, clinical datasets often suffer from high missingness, which adversely impact…
ImputationMissing ValuesAdvancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability
It is imperative that breast cancer is detected precisely and timely to improve patient outcomes. Diagnostic methodologies have traditionally relied on unimodal approaches; however, medical data analytics is integrating …
Decision MakingDiagnosticIdentifying Epigenetic Signature of Breast Cancer with Machine Learning
The research reported in this paper identifies the epigenetic biomarker (methylation beta pattern) of breast cancer. Many cancers are triggered by abnormal gene expression levels caused by aberrant methylation of CpG sit…
BIG-bench Machine LearningDetecting Breast Cancer using a Compressive Sensing Unmixing Algorithm
Traditional breast cancer imaging methods using microwave Nearfield Radar Imaging (NRI) seek to recover the complex permittivity of the tissues at each voxel in the imaging region. This approach is suboptimal, in that it…
Compressive SensingTowards Non-invasive and Personalized Management of Breast Cancer Patients from Multiparametric MRI via A Large Mixture-of-Modality-Experts Model
Breast magnetic resonance imaging (MRI) is the imaging technique with the highest sensitivity for detecting breast cancer and is routinely used for women at high risk. Despite the comprehensive multiparametric protocol o…
Management