Machine learning in infectious diseases: potential applications and limitations

Figures & data

Table 1. Summary of reviewed studies on the use of ML in infectious diseases.

Disease	Topic	Model algorithms	References
Various	Disease classification from biochemical data	Light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost) and Deepl Neural Network (DNN)	Park et al. [3]
Bloodstream infections	Disease classification from biochemical data and patient clinic history	LightGBM	Zoabi et al. [4]
Malaria	Outbreak prediction from environmental data	Linear regression, Logistic regression, Neural networks, XGBoost, K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naïve Bayes	Kalipe et al. [7]
Dengue	Outbreak prediction from environmental data	Categorical Boosting (CatBoost), Support Vector Machine (SVM), Long Short-Term Memory (LSTM)	Sebastianelli et al. [8]
COVID-19	Outbreak prediction from environmental data	Convolution Neural Network (CNN), ADtree Classifier and BayesNet	Abdulkareem et al. [9]
Dengue and Flu	Outbreak prediction from social networks data	Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT)	Amin et al. [10]
COVID-19	Disease severity prediction from heterogeneous data	J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR)	Moulaei et al. [11]

Figure 1. Challenges to ML in infectious diseases and potential approaches and applications.

Park DJ, Park MW, Lee H, et al. Development of machine learning model for diagnostic disease prediction based on laboratory tests. Sci Rep. 2021;11(1):7567. doi:10.1038/s41598-021-87171-5.

 PubMed Web of Science ®Google Scholar

Zoabi Y, Kehat O, Lahav D, et al. Predicting bloodstream infection outcome using machine learning. Sci Rep. 2021;11(1):20101. doi:10.1038/s41598-021-99105-2.

 PubMed Web of Science ®Google Scholar

Kalipe G, Gautham V, Behera RK. Predicting malarial outbreak using machine learning and deep learning approach: a review and analysis. In Proceedings of the International Conference on Information Technology, ICIT. 2018; pp. 33–38. doi:10.1109/ICIT.2018.00019.

 Google Scholar

Sebastianelli A, Spiller D, Carmo R, et al. A reproducible ensemble machine learning approach to forecast dengue outbreaks. Sci Rep. 2024;14(1):3807. doi:10.1038/s41598-024-52796-9.

 PubMed Web of Science ®Google Scholar

Abdulkareem AB, Sani NS, Sahran S, et al. Predicting COVID-19 based on environmental factors with machine learning. Intell Autom Soft Comput. 2021;28(2):305–320. doi:10.32604/iasc.2021.015413.

 Web of Science ®Google Scholar

Amin S, Uddin MI, Alsaeed DH, et al. Early detection of seasonal outbreaks from twitter data using machine learning approaches. Complexity. 2021;2021:1–12. doi:10.1155/2021/5520366.

 Web of Science ®Google Scholar

Moulaei K, Shanbehzadeh M, Mohammadi-Taghiabad Z, et al. Comparing machine learning algorithms for predicting COVID-19 mortality. BMC Med Inform Decis Mak. 2023;22(1):1–12. doi:10.1186/S12911-021-01742-0.

 Web of Science ®Google Scholar

Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.