Impact of Comprehensive Data Preprocessing on Predictive Modelling of COVID-19 Mortality

TL;DR

This study demonstrates that a tailored data preprocessing pipeline significantly improves the accuracy of machine learning models predicting COVID-19 mortality, emphasizing the importance of customized data handling techniques.

Contribution

The paper introduces a novel, comprehensive preprocessing pipeline that enhances predictive accuracy for COVID-19 mortality models beyond standard methods.

Findings

Custom pipeline improves model performance (e.g., RMSE and R-squared)

MLP Regressor outperforms DecisionTree in this context

Tailored preprocessing techniques are valuable for predictive modeling

Abstract

Accurate predictive models are crucial for analysing COVID-19 mortality trends. This study evaluates the impact of a custom data preprocessing pipeline on ten machine learning models predicting COVID-19 mortality using data from Our World in Data (OWID). Our pipeline differs from a standard preprocessing pipeline through four key steps. Firstly, it transforms weekly reported totals into daily updates, correcting reporting biases and providing more accurate estimates. Secondly, it uses localised outlier detection and processing to preserve data variance and enhance accuracy. Thirdly, it utilises computational dependencies among columns to ensure data consistency. Finally, it incorporates an iterative feature selection process to optimise the feature set and improve model performance. Results show a significant improvement with the custom pipeline: the MLP Regressor achieved a test RMSE of 66.556 and a test R-squared of 0.991, surpassing the DecisionTree Regressor from the standard pipeline, which had a test RMSE of 222.858 and a test R-squared of 0.817. These findings highlight the importance of tailored preprocessing techniques in enhancing predictive modelling accuracy for COVID-19 mortality. Although specific to this study, these methodologies offer valuable insights into diverse datasets and domains, improving predictive performance across various contexts.