Ovarian Cancer Classification Using Hybrid Synthetic Minority Over-Sampling Technique and Neural Network

Abstract Every woman is at risk of ovarian cancer; about 90 percent of women who develop ovarian cancer are above 40 years of age, with the high number of ovarian cancers occurring at the age of 60 years and above. Early and correct diagnosis of ovarian cancer can allow proper treatment and as a result reduce the mortality rate. In this paper, we proposed a hybrid of Synthetic Minority Over-Sampling Technique (SMOTE) and Artificial Neural Network (ANN) to diagnose ovarian cancer from public available ovarian dataset. The dataset were firstly preprocessed using SMOTE before employing Neural Network for classification. This study shows that performance of Neural networks in the cancer classification is improved by employing SMOTE preprocessing algorithm to reduce the effect of data imbalance in the dataset. To justify the performance of the proposed approach, we compared our results with the standard neural network algorithms. The performance measurement evaluated was based on the accuracy, F-measure, Recall, ROC Area Margin Curve and Precision. The results showed that SMOTE + MLP (with above 96% accuracy) performed better than SMOTE + RBF and standard RBF and MLP.