Feature Engineering Combined with 1 D Convolutional Neural Network for Improved Mortality Prediction

TL;DR

This study enhances ICU mortality prediction by combining feature engineering with a 1-D CNN, outperforming traditional models in accuracy on a challenging clinical dataset.

Contribution

It introduces a novel approach integrating feature engineering with 1-D CNN for improved mortality prediction in ICU patients.

Findings

1-D CNN achieved an AUC of 0.848.

Feature engineering improved model performance.

Compared favorably to traditional machine learning algorithms.

Abstract

The intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of Electronic Health Record (EHR). This data allows for the development of a prediction tool with perfect knowledge backing. We aimed to build a mortality prediction model on 2012 Physionet Challenge mortality prediction database of 4000 patients admitted in ICU. The challenges in the dataset, such as high dimensionality, imbalanced distribution, and missing values were tackled with analytical methods and tools via feature engineering and new variable construction. The objective of the research is to utilize the relations among the clinical variables and construct new variables which would establish the effectiveness of 1-Dimensional Convolutional Neural Network (1- D CNN) with constructed features. Its performance with the traditional machine learning algorithms like XGBoost classifier, Support Vector Machine (SVM), K-Neighbours Classifier (K-NN), and Random Forest Classifier (RF) is compared for Area Under Curve (AUC). The investigation reveals the best AUC of 0.848 using 1-D CNN model.