Evaluation of Hemogram Parameters for COVID-19 Diagnosis Based on Machine Learning and Heuristic Methods

A pandemic outbreak emerged in Wuhan, China, in December 2019. Shortly after that, the World Health Organization (WHO) identified the causative agent as a novel member of the Coronavirus family. Genetic analysis indicated that the SARS-CoV-2 virus was closely related to severe acute respiratory syndrome (SARS). The disease caused by SARS-CoV-2 is termed COVID-19. This study analyzes hemogram parameters and COVID-19 Polymerase Chain Reaction (PCR) test results using various machine-learning (ML) techniques. The ML techniques applied are Support Vector Machine, Adaptive Boosting, Gradient Boosting, Light Gradient Boosting Machine, Extreme Gradient Boosting, and Random Forest. In addition, particle swarm optimization and genetic algorithm are used for hyperparameter optimization. Both heuristic algorithms demonstrated capable search trajectories in optimizing ensemble tree parameters, effectively mapping the complex nonlinear optimization landscape. It was observed that using only the ten most valuable parameters did not significantly affect the results. Additionally, a trapezoidal membership function was created to simulate decision-making processes. The original dataset was compared with a dataset modified using trapezoidal membership functions, with degrees set according to the decision-makers' reference ranges. The results showed that data loss due to processing with reference intervals negatively impacted the results. Given the delayed response to COVID-19 in Latin American countries, the datasets for this study were collected from this region. The training dataset for the machine learning model was collected from Ecuador and Peru. External validation was performed using a new, unique dataset. This regional focus highlights the importance of sustainable economic measures to enhance the resilience of health systems in Latin America and facilitate better resource management in future outbreaks.