Prediction of Malignant and Benign Breast Cancer: A Data Mining Approach in Healthcare Applications

2020 ◽  
pp. 435-442 ◽  
Author(s):  
Vivek Kumar ◽  
Brojo Kishore Mishra ◽  
Manuel Mazzara ◽  
Dang N. H. Thanh ◽  
Abhishek Verma
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Healthcare Applications ◽  
Data Mining Approach

Evaluating the impact of soy compounds on breast cancer using the data mining approach

2020 ◽  
Vol 11 (5) ◽  
pp. 4561-4570
Author(s):  
Sheng-I Chen ◽  
Hsiao-Ting Tseng ◽  
Chia-Chien Hsieh
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Cancer Type ◽  
Data Mining Approach ◽  
The Impact

Accumulating evidence has shown that soy intake is associated with the prevention of cancers. However, the specific soy compound and cancer type should be considered before allocating a precise nutrient intervention.

scholarly journals Decision Tree Classification and Model Evaluation for Breast Cancer Survivability: A Data Mining Approach

2017 ◽  
Vol 10 (1) ◽  
pp. 281-289 ◽  
Author(s):  
Chinnaiyan Ponnuraja ◽  
Babu C Lakshmanan ◽  
Valarmathi Srinivasan ◽  
Krihsna Prasanth B
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Decision Tree ◽  
Model Evaluation ◽  
Decision Tree Classification ◽  
Data Mining Approach

Proximate Breast Cancer Factors Using Data Mining Classification Techniques

2019 ◽  
Vol 4 (1) ◽  
pp. 47-56
Author(s):  
Alice Constance Mensah ◽  
Isaac Ofori Asare
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Random Forest ◽  
Cancer Patients ◽  
Classification Model ◽  
Tree Model ◽  
Cancer Data ◽  
Data Mining Approach ◽  
Learning Techniques ◽  
Using Data

Breast cancer is the most common of all cancers and is the leading cause of cancer deaths in women worldwide. The classification of breast cancer data can be useful to predict the outcome of some diseases or discover the genetic behavior of tumors. Data mining technology helps in classifying cancer patients and this technique helps to identify potential cancer patients by simply analyzing the data. This study examines the determinant factors of breast cancer and measures the breast cancer patient data to build a useful classification model using a data mining approach. In this study of 2397 women, 1022 (42.64%) were diagnosed with breast cancer. Among the four main learning techniques such as: Random Forest, Naive Bayes, Classification and Regression Model (CART), and Boosted Tree model were used for the study. The Random Forest technique had the better accuracy value of 0.9892(95%CI,0.9832 -0.9935) and a sensitivity value of about 92%. This means that the Random Forest learning model is the best model to classify and predict breast cancer based on associated factors.

A Data Mining Approach for the Detection of High-Risk Breast Cancer Groups

2010 ◽  
pp. 43-51 ◽  
Author(s):  
Orlando Anunciação ◽  
Bruno C. Gomes ◽  
Susana Vinga ◽  
Jorge Gaspar ◽  
Arlindo L. Oliveira ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
High Risk ◽  
Data Mining Approach ◽  
High Risk Breast Cancer ◽  
High Risk Breast ◽  
Risk Breast Cancer

scholarly journals A Proposed Method for Mining Breast Cancer Pattern Using Particle Swarm Optimization

2019 ◽  
Vol 8 (1) ◽  
pp. 69-73
Author(s):  
Pranjali Dewangan ◽  
Neelamsahu .
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Particle Swarm Optimization ◽  
Hybrid Approach ◽  
Particle Swarm ◽  
System Structure ◽  
Second Phase ◽  
Data Mining Algorithm ◽  
Swarm Optimization ◽  
Data Mining Approach

Breast cancer is one of the leading causes of death among women in many parts of the world. In this paper, we have developed an efficient hybrid data mining approach to separate from a population of patients who have and who do not have breast cancer. The proposed data mining approach has consisted of two phases. In first phase, the statistical method will be used to pre-process the data, which can eliminate the insignificant features. It can reduce the computational complexity and speed up the data mining process. In the second phase, we proposed a new data mining methodology, which based on the fundamental concept of the standard particle swarm optimization (PSO), namely discrete PSO. This phase aimed at creating a novel PSO in which each particle was coded in positive integer numbers and had a feasible system structure. Based on the obtained results, our proposed DPSO can improve the accuracy to 98.71%, sensitivity to 100%, and specificity to 98.21%. When compared with the previous research, the proposed hybrid approach shows the improvement in both accuracy and robustness. According to the high quality of our research results, the proposed DPSO data mining algorithm can be used as the reference for deciding on hospital and provide the reference for the researchers.

A hybrid data mining approach for identifying the temporal effects of variables associated with breast cancer survival

2020 ◽  
Vol 139 ◽  
pp. 112863 ◽  
Author(s):  
Serhat Simsek ◽  
Ugur Kursuncu ◽  
Eyyub Kibis ◽  
Musheera AnisAbdellatif ◽  
Ali Dag
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Temporal Effects ◽  
Data Mining Approach ◽  
Hybrid Data
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