Decision Trees for the Analysis of Gene Expression Levels of COVID-19: An Association with Alzheimer’s Disease

COVID-19 has caused millions of deaths around the world. The respiratory system is the main target of this disease, but it has also been reported to attack the central nervous system, creating a neuroinflammatory environment with the release of proinflammatory cytokines. There are several studies suggesting a possible relationship between Alzheimer’s disease and COVID-19. Therefore, in this study, machine learning microarray analysis was performed to identify key genes in COVID-19 that may be associated with Alzheimer’s disease. The dataset is identified as GSE177477, containing 47 samples. A bioconductor oligo package in the RStudio (version 4.3.3) environment was used to process and normalize the data. Subsequently, one-way ANOVA was used to obtain differentially expressed genes. We used decision tree generation to classify 47 samples. The study identified 1856 differentially expressed genes. Three decision trees were generated where three genes (DNAJC16, TREM1, and UCP2) were identified that differentiated patients. The best decision tree obtained an accuracy of 72.34%, with a sensitivity of 72.34% and a specificity of 86.17%. The genes identified with the decision trees may be involved in processes like those of Alzheimer’s disease, such as in the inflammation process, amyloid pathologies, and related to type 2 diabetes mellitus.