Convolutional Neural Networks in Multi-Class Classification of Medical Data

TL;DR

This paper explores the application of CNNs to multi-class medical data classification, demonstrating how model and data adjustments affect accuracy and introducing an ensemble approach that outperforms individual models.

Contribution

It presents a novel ensemble model combining CNN and Gradient Boosting for improved multi-class medical data classification accuracy.

Findings

Achieved 64.93% overall accuracy

Ensemble model outperforms individual CNN and Gradient Boosting models

CNN and ensemble models have higher Recall than Precision

Abstract

We report applications of Convolutional Neural Networks (CNN) to multi-classification classification of a large medical data set. We discuss in detail how changes in the CNN model and the data pre-processing impact the classification results. In the end, we introduce an ensemble model that consists of both deep learning (CNN) and shallow learning models (Gradient Boosting). The method achieves Accuracy of 64.93, the highest three-class classification accuracy we achieved in this study. Our results also show that CNN and the ensemble consistently obtain a higher Recall than Precision. The highest Recall is 68.87, whereas the highest Precision is 65.04.