Impact of Comprehensive Data Preprocessing on Predictive Modelling of COVID-19 Mortality
This work addresses the need for accurate predictive models for COVID-19 mortality trends, offering incremental improvements through tailored preprocessing techniques.
This study tackled the problem of predicting COVID-19 mortality by evaluating a custom data preprocessing pipeline on ten machine learning models, resulting in significant improvements such as the MLP Regressor achieving a test RMSE of 66.556 and R-squared of 0.991 compared to a standard pipeline's DecisionTree Regressor with RMSE of 222.858 and R-squared of 0.817.
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.