Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases

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. Moreover, heterogeneous data could ensure the model’s generalization, while big data, many features, and a hybrid model will increase the resulting performance. Furthermore, using other techniques such as deep learning and NLP to extract vast features from unstructured data is a powerful approach to enhancing the performance of ML diagnostic models.

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

“…The most common ML methods in the reviewed studies were CNN 34–85 . In line with this result, other review studies also indicated CNN to be the most common method to develop a system for COVID‐19 diagnosis 23,25,27,28,30,100 . A CNN is a type of DL algorithm used for processing medical images, particularly for identifying specific features in chest radiographs of COVID‐19 patients 30 .…”

“…In line with this result, other review studies also indicated CNN to be the most common method to develop a system for COVID-19 diagnosis. 23,25,27,28,30,100 A CNN is a type of DL algorithm used for processing medical images, particularly for identifying specific features in chest radiographs of COVID-19 patients. 30 CNN is more valuable than other methods for developing CDSS due to its excellent performance accuracy and much lower preprocessing.…”

“…20,22,23 A type of ML is deep learning (DL), which uses multiple-layer artificial neural networks. 20,22,23 One of the most widely used subsets of DL is the Convolutional Neural Network (CNN), which allows the system to learn data representation. 20,22,23 Previous review studies have examined the impact of AI or ML models in screening, diagnostics, and prediction of COVID-19, [24][25][26][27][28] ML models for diagnosing infectious diseases, 23 CDSS based on AI for early recognition of respiratory infections, 29 and CNN for the diagnosis and prognosis of COVID-19.…”

“…The ICDSS based on ML or nonknowledge‐based CDSS learn to solve human problems by simulating human learning on a computer 20,22 . ML is the ability of machines to find patterns and learn hidden knowledge from large data sets using analytical techniques 20,22,23 . The goal is to teach automated techniques to classify and cluster data, learn behavior, generate patterns, and predict future actions using decision support systems 20,22,23 .…”

Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment

“…Currently, machine learning is widely accepted due to its ability to develop prediction models, including offering more flexible modeling and its ability to analyze ‘big’, non-linear, and high dimensional data as well as to model complex clinical scenarios [ 11 ]. These approaches can be efficiently tested in healthcare applications, such as disease diagnosis, medical image analysis, big data collection, research and clinical trials, management of smart health records, and prediction of disease outbreaks [ 12 ]. The proposal of new prognostication systems using machine learning is expected to increase in the near future; therefore, it is necessary to critically evaluate these emerging methods.…”