Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases

TL;DR

This paper reviews recent machine-learning models for early infectious disease diagnosis, highlighting their strengths, limitations, and the importance of dataset characteristics for model effectiveness.

Contribution

It provides a comprehensive review of ML algorithms applied to infectious disease diagnosis from 2015 to 2022, emphasizing dataset limitations and future research directions.

Findings

Most studies used small datasets

Few models utilized real-time data

Model effectiveness depends on dataset nature

Abstract

Controlling infectious diseases is a major health priority because they can spread and infect humans, thus evolving into epidemics or pandemics. Therefore, early detection of infectious diseases is a significant need, and many researchers have developed models to diagnose them in the early stages. This paper reviewed research articles for recent machine-learning (ML) algorithms applied to infectious disease diagnosis. We searched the Web of Science, ScienceDirect, PubMed, Springer, and IEEE databases from 2015 to 2022, identified the pros and cons of the reviewed ML models, and discussed the possible recommendations to advance the studies in this field. We found that most of the articles used small datasets, and few of them used real-time data. Our results demonstrated that a suitable ML technique depends on the nature of the dataset and the desired goal.