A diagnostic model for COVID-19 based on proteomics analysis.

ABSTRACT:

Background and objective

Early diagnosis of Coronavirus Disease 2019 (COVID-19) can help save patients' lives before the disease turns severe. This can be achieved through an effective and correct treatment protocol. In this paper, a prediction model is proposed to detect infected cases and determine the severity level of the disease.

Methods

The proposed model is based on utilizing proteins and metabolites as features for each patient, which are then analyzed using feature selection methods such as Principal Component Analysis (PCA), Information Gain (IG), and analysis of Variance (ANOVA) to select the most significant features. The model employs three classifiers, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), to predict and classify

SUBMITTER: Alkady W 

PROVIDER: S-EPMC10232940 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature