COMPARATIVE ANALYSIS OF CLASSIFICATION APPROACHES FOR BREAST CANCER

Abstract

Breast cancer is one of the greatest common diseases among women in Africa and worldwide. Accurate and early diagnosis is very significant phase in therapy and action. However, it is not an easy one due to some doubts in detection of breast cancer. Machine learning helps us to extract information and knowledge from this the basis of past experiences and detect hard-to-perceive pattern from large and noisy dataset. This paper compares and analysis the performance of machine learning algorithms, namely Decision Tree (DT), Logistic Regression (LR), Naïve Bayes (NB), and K-Nearest Neighbors (KNN) for detecting breast cancer. The data set used for comparison was from UCI Wisconsin original breast cancer data set. The result outcome shows that Logistic Regression performs better and classification accuracy is 96.93%.