A comparison between different classification algorithms for predicting metastasis in breast cancer patients

Breast cancer is one of the most common cancers among women around the world. According to World Health Organization (WHO), breast cancer is second reason for cancer mortality. Approximately 30%- 40% patients suffering from breast cancer will experience recurrence and 10%-15% of them were reported to die of cancer metastasis. Early diagnosis or prediction of metastasis will reduce mortality rate and treatment cost. In this study we have used a data set containing 555 record of patients with breast cancer (83 have experienced metastasis) and 8 features. Several machine Learning algorithms including Random Forest (RF), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Multi-Layer Perceptron (MLP) were used to predict metastasis. Total accuracy, sensitivity, specificity, precision, recall, f1- score and area under curve (AUC) extracted out of Receiver operating characteristic values were used to evaluate models. The results show that Multi-Layer Perceptron Outperform other methods to predict the metastasis.

Keywords:

Breast Cancer , Metastasis , Prediction , Machine Learning , Deep Learning