Breast Cancer Risk Prediction Using Data Mining Classification Techniques

Abstract

Breast cancer poses serious threat to the lives of people and it is the second leading cause of death in women today and the most common cancer in women in developing countries in Nigeria where there are no services in place to aid the early detection of breast cancer in Nigerian women. A number of studies have been undertaken in order to understand the prediction of breast cancer risks using data mining techniques. Hence, this study is focused at using two data mining techniques to predict breast cancer risks in Nigerian patients using the naïve bayes' and the J48 decision trees algorithms. The performance of both classification techniques was evaluated in order to determine the most efficient and effective model. The J48 decision trees showed a higher accuracy with lower error rates compared to that of the naïve bayes' method while the evaluation criteria proved the J48 decision trees to be a more effective and efficient classification techniques for the prediction of breast cancer risks among patients of the study location.

A Survey on the Data Mining Techniques Used for Breast Cancer Predictions

“…Breast cancer is caused by a number of factors called risk factors; they are classified as either modifiable (those that can be controlled like habits, environmental hazards, etc) or nonmodifiable factors (those that cannot be controlled like, gender, family history etc) [3]. Most women have more than one known risk factor for breast cancer, yet will never get the disease.…”

“…Data mining is the process of deriving patterns from data; the patterns that may be discovered depend on the data mining tasks that are performed on the dataset. There are two basic data mining tasks:  descriptive data mining tasks, which help to understand the specific properties of the dataset  predictive data mining tasks, which are used to perform predictions on the available dataset [3].…”

“…The specific algorithm is selected based on the particular objective to be achieved and the quality of data to be analyzed.  Evaluation and deployment: This is the analysis and interpretation of the results gotten from the analysis done by the data mining algorithms in order to create recommendations for consideration [3]. Fig.…”

A Novel Iterative Linear Regression Perceptron Classifier for Breast Cancer Prediction

“…Williams et al [1] have focused at two data mining techniques namely naïve bayes and j48 decision trees to predict breast cancer risks in Nigerian patients. The analysis is made to determine the most efficient and effective model.…”

“…Modifiable factors are those that can be controlled like habitual and environmental issues. Non-modifiable factors are those that cannot be controlled like gender and family history [1]. According to a survey, 1 out of 28 women in India is prone to breast cancer as the early detection techniques on the presence of breast cancer are still lacking in the exact prediction of disease.…”