Predicting the Occurrence of Advanced Schistosomiasis Based on FISHER Discriminant Analysis of Hematological Biomarkers

Abstract

We established a model that predicts the possibility of chronic schistosomiasis (CS) patients developing into advanced schistosomiasis (AS) patients using special biomarkers that were detected in human peripheral blood. Blood biomarkers from two cohorts (132 CS cases and 139 AS cases) were examined and data were collected and analyzed by univariate and multivariate logistic regression analysis. Fisher discriminant analysis (FDA) for advanced schistosomiasis was established based on specific predictive diagnostic indicators and its accuracy was assessed using data of 109 CS. The results showed that seven indicators including HGB, MON, GLB, GGT, APTT, VIII, and Fbg match the model. The accuracy of the FDA was assessed by cross-validation, and 86.7% of the participants were correctly classified into AS and CS groups. Blood biomarker data from 109 CS patients were converted into the discriminant function to determine the possibility of occurrence of AS. The results demonstrated that the possibility of occurrence of AS and CS was 62.1% and 89.0%, respectively, and the accuracy of the established model was 81.4%. Evidence displayed that Fisher discriminant analysis is a reliable predictive model in the clinical field. It's an important guide to effectively control the occurrence of AS and lay a solid foundation for achieving the goal of schistosomiasis elimination.

Keywords: advanced schistosomiasis; biomarkers; fisher discriminant analysis; predicting; prediction model.