Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing.

ABSTRACT:

Background

Amebiasis represents a significant global health concern. This is especially evident in developing countries, where infections are more common. The primary diagnostic method in laboratories involves the microscopy of stool samples. However, this approach can sometimes result in the misinterpretation of amebiasis as other gastroenteritis (GE) conditions. The goal of the work is to produce a machine learning (ML) model that uses laboratory findings and demographic information to automatically predict amebiasis.

Method

Data extracted from Jordanian electronic medical records (EMR) between 2020 and 2022 comprised 763 amebic cases and 314 nonamebic cases. Patient demographics, clinical signs, microscopic diagnoses, and leukocyte counts were used to train eight decision

SUBMITTER: Al-Khlifeh E 

PROVIDER: S-EPMC11780931 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature