Abstract
Blood tests play an essential role in everyday medicine and are used by doctors in several diagnostic procedures. Moreover, this data is multivariate – and often some diseases, such as COVID-19, could have different symptom manifestations and outcomes. This study proposes a method of extracting useful information from blood tests using UMAP technique – Uniform Manifold Approximation and Projection for Dimension Reduction combined with DBSCAN clustering and statistical approaches. The analysis performed here indicates several clusters of infection prevalence varying between 2%–37%, showing that our procedure is indeed capable of finding different patterns. A possible explanation is that COVID-19 is not just a respiratory infection but a systemic disease with critical hematological implications, primarily on white-cell fractions, as indicated by relevant statistical test
-values in the range of 0.03–0.1. The novel analysis procedure proposed could be adopted in other data-sets of different illnesses to help researchers to discover new patterns of data that could be used in various diseases and contexts.
【저자키워드】 COVID-19, machine learning, unsupervised learning, Dimensionality reduction, Blood exam, Applied AI, 【초록키워드】 Diseases, Symptom, outcomes, respiratory infection, Clustering, Cluster, information, disease, Critical, blood test, Analysis, UMAP, manifestation, diagnostic procedures, Implications, approaches, projection, help, hematological, infection prevalence, Fractions, approximation, dimension, researcher, statistical, performed, indicated, indicate, adopted, illness, statistical test, data-set, DBSCAN, Manifold, Uniform, 【제목키워드】 Blood,