Diagnosis of Breast Cancer on Imbalanced Dataset Using Various Sampling Techniques and Machine Learning Models
Ruchita Gupta, Rupal Bhargava, Manoj Jayabalan
Abstract
Breast Cancer is the second most leading cause of death among women. The early detection of the disease increases the chances of survival of the patient. Therefore, there is always a need for techniques that can accurately predict the presence of cancer. Data Mining is one such powerful technique that can assist clinicians to effectively use the data for timely prediction of the disease. In the medical domain, data is usually imbalanced with unequal distribution of the positive and negative classes. Imbalanced datasets introduce a bias in the model and can thus reduce the accuracy of the minority class predictions. In the case of cancer detection, the mammographic data is highly imbalanced, and predicting the positive (minority) class is of the utmost importance. To achieve this, different models using various class balancing techniques are built and evaluated. The experiments show that the performance of the weighted approach and the undersampling technique is better than oversampling and hybrid techniques. The best performing classifiers are the weighted XGBoost model and Stacking ensemble with the average AUC of 0.78 and 0.76 respectively.