Improving Performance of Machine Learning on Prediction of Breast Cancer Over a Small Sample Dataset

Abstract

The application of machine learning (ML) algorithms aim to develop prognostic tools that could be trained on data that is routinely collected. In a typical scenario, the ML algorithm-based prognostic tool is utilized to search through large volumes of data to look for complex relationships in the training data. However, not much attention has been devoted to scenarios where small sample datasets are a widespread occurrence in research areas involving human participants such as clinical trials, genetics, and neuroimaging. In this research, we have studied the impact of the size of the sample dataset on the model performance of different ML algorithms. We compare the model fitting and model prediction performance on the original small dataset and the augmented dataset. Our research has discovered that the model fitted on a small dataset exhibits severe overfitting during the testing stage, which reduces when the model is trained on the augmented dataset. However, to different ML algorithms, the improvement of the model performance due to trained by the augmented dataset may vary.