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Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel.

Fariha Ahmed Nishat¹, M F Mridha², Istiak Mahmud³

¹Dhaka National Medical College, Dhaka 1100, Bangladesh.

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|March 13, 2025

Summary

This summary is machine-generated.

A new machine learning tool accurately detects typhoid fever using common clinical data. This cost-effective method offers rapid diagnosis, improving patient care in resource-limited areas.

Keywords:

clinical data analysis ensemble learning machine learning metamodel non-invasive diagnostics predictive modeling typhoid fever diagnosis

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Area of Science:

Computational biology and bioinformatics
Infectious disease diagnostics
Machine learning applications in healthcare

Background:

Typhoid fever is a major global health concern, particularly in developing nations with limited diagnostic infrastructure.
Current diagnostic methods for typhoid fever are often slow and require significant resources.
Early and precise diagnosis is vital for effective treatment and controlling the spread of typhoid fever.

Purpose of the Study:

To develop a lightweight machine learning (ML) diagnostic tool for early and efficient detection of typhoid fever.
To utilize readily available clinical and demographic data for typhoid fever diagnosis.
To create a cost-effective and non-invasive diagnostic alternative for resource-limited settings.

Main Methods:

A custom dataset of 14 clinical and demographic parameters was analyzed.
A machine learning metamodel, combining Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Decision Tree classifiers with Light Gradient Boosting Machine (LGBM), was developed.
The model was trained and validated using k-fold cross-validation, with performance metrics including precision, recall, F1-score, and AUC.

Main Results:

The proposed ML metamodel achieved exceptional diagnostic performance: 99% precision, 100% recall, and an Area Under the Curve (AUC) of 1.00.
The model demonstrated superior accuracy and generalizability compared to traditional methods and standalone ML algorithms.
The developed tool is lightweight, cost-effective, and non-invasive.

Conclusions:

The lightweight ML metamodel presents a viable, rapid, and accessible diagnostic solution for typhoid fever, especially in resource-constrained environments.
Its reliance on common clinical parameters ensures practical application and scalability.
Further validation and integration into clinical workflows are recommended to maximize its impact on patient outcomes and disease control.