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

Enas Al-Khlifeh¹, Ahmad S Tarawneh², Khalid Almohammadi³

¹Department of Applied Biology, Al-Balqa Applied University, Salt, Jordan. Al-khlifeh.en@bau.edu.jo.

Parasites & Vectors

|January 29, 2025

Summary

Machine learning accurately predicts amebiasis using electronic medical records, improving diagnosis over traditional microscopy. This aids in distinguishing parasitic infections from gastroenteritis.

Keywords:

E. histolytica Amebiasis Decision tree Electronic medical records (EMR)Feature selection Jordan Leukocytosis Machine learning Microscopic diagnosis Stool RBCs

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Area of Science:

Medical informatics
Computational biology
Parasitology

Background:

Amebiasis is a global health issue, often diagnosed via microscopy, which can be confused with gastroenteritis.
Accurate diagnosis is crucial, especially in developing nations with high infection rates.

Purpose of the Study:

To develop a machine learning (ML) model for accurate amebiasis prediction.
Utilize laboratory findings and demographic data for automated diagnosis.

Main Methods:

Trained eight decision tree algorithms on Jordanian electronic medical records (2020-2022).
Included 763 amebic and 314 non-amebic cases, analyzing demographics, clinical signs, and lab results.
Employed feature ranking and correlation to enhance classification accuracy.

Main Results:

Key predictors for amebiasis include neutrophil percentage, mucus presence, and RBC/WBC counts in stool.
Decision tree models achieved 92%-94.6% accuracy; an optimized Random Forest model showed a 98% AUC.
Amebiasis accounted for 17.22% of gastroenteritis cases in Jordan, with higher incidence in males and young children.

Conclusions:

Machine learning applied to electronic medical records accurately predicts amebiasis.
This supports ML's role as a decision support tool in diagnosing parasitic diseases.