Predicting the Risk of Lymphedema in Breast Cancer Patients by Using Data Mining Techniques

Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients’ survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.

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A Novel Data Mining on Breast Cancer Survivability Using MLP Ensemble Learners

The Computer Journal ◽

10.1093/comjnl/bxz051 ◽

2019 ◽

Vol 63 (3) ◽

pp. 435-447

Author(s):

Mohsen Salehi ◽

Jafar Razmara ◽

Shahriar Lotfi

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Data Mining ◽

Cancer Patients ◽

Cancer Incidence ◽

The United States ◽

Machine Learning Techniques ◽

Breast Cancer Patients ◽

Stacked Generalization ◽

Learning Techniques

Abstract Breast cancer survivability has always been an important and challenging issue for researchers. Different methods have been utilized mostly based on machine learning techniques for prediction of survivability among cancer patients. The most comprehensive available database of cancer incidence is SEER in the United States, which has been frequently used for different research purposes. In this paper, a new data mining has been performed on the SEER database in order to investigate the ability of machine learning techniques for survivability prediction of breast cancer patients. To this end, the data related to breast cancer incidence have been preprocessed to remove unusable records from the dataset. In sequel, two machine learning techniques were developed based on the Multi-Layer Perceptron (MLP) learner machine including MLP stacked generalization and mixture of MLP-experts to make predictions over the database. The machines have been evaluated using K-fold cross-validation technique. The evaluation of the predictors revealed an accuracy of 84.32% and 83.86% by the mixture of MLP-experts and MLP stacked generalization methods, respectively. This indicates that the predictors can be significantly used for survivability prediction suggesting time- and cost-effective treatment for breast cancer patients.

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Prediction of Benign and Malignant Breast Cancer Using Data Mining Techniques

SSRN Electronic Journal ◽

10.2139/ssrn.3139141 ◽

2018 ◽

Author(s):

Vikas Chaurasia ◽

Saurabh Pal

Keyword(s):

Breast Cancer ◽

Data Mining ◽

Data Mining Techniques ◽

Malignant Breast ◽

Using Data

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Using Data Mining Techniques to Support Breast Cancer Diagnosis

New Contributions in Information Systems and Technologies - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-319-16486-1_68 ◽

2015 ◽

pp. 689-700 ◽

Cited By ~ 7

Author(s):

Joana Diz ◽

Goreti Marreiros ◽

Alberto Freitas

Keyword(s):

Breast Cancer ◽

Data Mining ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis ◽

Data Mining Techniques ◽

Using Data

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Proximate Breast Cancer Factors Using Data Mining Classification Techniques

International Journal of Big Data and Analytics in Healthcare ◽

10.4018/ijbdah.2019010104 ◽

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.

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