A robust innovative pipeline-based machine learning framework for predicting COVID-19 in Mexican patients

The emergence of COVID-19 in late 2019 in Wuhan, China, has led to a global health crisis that has claimed many lives worldwide. A thorough understanding of the available COVID-19 datasets can enable healthcare professionals to identify cases at an early stage. This study presents an innovative pipeline-based framework for predicting survival and mortality in patients with COVID-19 by leveraging the Mexican COVID-19 patient dataset (COVID-19-MPD dataset). Preprocessing plays a pivotal role in ensuring that the framework delivers high-quality outcomes. We deploy various machine learning models with optimized hyperparameters within the framework. Through consistent experimental conditions and dataset utilization, we conducted multiple experiments employing diverse preprocessing techniques and models to maximize the area under the receiver operating characteristic curve (AUC) for COVID-19 prediction. Given the considerable dimensions of the dataset, feature selection is crucial for identifying factors influencing COVID-19 mortality or survival. We employ feature dimension reduction methods, such as principal component analysis and independent component analysis, in addition to feature selection techniques such as maximum relevance minimum redundancy and permutation feature importance. Impactful features related to patient outcomes can significantly aid experts in disease management by enhancing treatment efficacy and control measures. Following various experiments with standardized data and AUC assessment using the k-nearest neighbor algorithm with four components, the proposed framework achieves optimal results, attaining an AUC of 100%. Given its effectiveness in COVID-19 prediction, this framework has the potential for integration into medical decision support systems.