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.

Most infectious diseases are caused by viruses, fungi, bacteria and parasites. Their ability to easily infect humans and trigger large-scale epidemics makes them a public health concern. Methods for early detection of these diseases have been developed; however, they are hindered by the absence of a unified, interoperable and reusable model. This study seeks to create a holistic and real-time model for swift, preliminary detection of infectious diseases using symptoms and additional clinical data. In this study, we present a medical knowledge graph (MKG) that leverages multiple data sources to analyse connections between different nodes. Medical ontologies were used to enhance the MKG. We applied various graph algorithms to extract key features. The performance of multiple machine-learning (ML) techniques for influenza and hepatitis detection was assessed, selecting multi-layer perceptron (MLP) and random forest (RF) models due to their superior outcomes. The hyperparameters of both graph-based ML models were automatically fine-tuned. Both the graph-based MLP and RF models showcased the least loss and error rates, along with the most specific, accurate recall, precision and F1 scores. Their Matthews correlation coefficients were also optimal. When compared with existing ML techniques and findings from the literature, these graph-based ML models manifested superior detection accuracy. The graph-based MLP and RF models effectively diagnosed influenza and hepatitis, respectively. This underlines the potential of graph data science in enhancing ML model performance and uncovering concealed relationships in the MKG.

Background: COVID-19 is an infectious disease caused by SARS-CoV-2. The symptoms of COVID-19 vary from mild-to-moderate respiratory illnesses, and it sometimes requires urgent medication. Therefore, it is crucial to detect COVID-19 at an early stage through specific clinical tests, testing kits, and medical devices. However, these tests are not always available during the time of the pandemic. Therefore, this study developed an automatic, intelligent, rapid, and real-time diagnostic model for the early detection of COVID-19 based on its symptoms. Methods: The COVID-19 knowledge graph (KG) constructed based on literature from heterogeneous data is imported to understand the COVID-19 different relations. We added human disease ontology to the COVID-19 KG and applied a node-embedding graph algorithm called fast random projection to extract an extra feature from the COVID-19 dataset. Subsequently, experiments were conducted using two machine learning (ML) pipelines to predict COVID-19 infection from its symptoms. Additionally, automatic tuning of the model hyperparameters was adopted. Results: We compared two graph-based ML models, logistic regression (LR) and random forest (RF) models. The proposed graph-based RF model achieved a small error rate = 0.0064 and the best scores on all performance metrics, including specificity = 98.71%, accuracy = 99.36%, precision = 99.65%, recall = 99.53%, and F1-score = 99.59%. Furthermore, the Matthews correlation coefficient achieved by the RF model was higher than that of the LR model. Comparative analysis with other ML algorithms and with studies from the literature showed that the proposed RF model exhibited the best detection accuracy. Conclusion: The graph-based RF model registered high performance in classifying the symptoms of COVID-19 infection, thereby indicating that the graph data science, in conjunction with ML techniques, helps improve performance and accelerate innovations.

Objective: Vectors that cause infectious lung diseases encompass viral, bacterial, fungal, and parasitic agents. Early detection of these infections is critical for timely diagnosis and effective treatment. Several studies have created solutions for early detection with varying performance, but with limitations such as image type specificity, lack of generalizability, potential overfitting, and bias problems. Our model effectively addresses these problems by using diverse image types, enhancing robustness, and generalizability across various contexts that aim for effective performance. Methods: This study creates an early detection model that works with both CT scans and X-ray images. We applied a convolutional neural network (CNN) model trained on diverse and large augmented datasets with fewer parameters. We then used a generative adversarial network (GAN) to validate our CNN model and create generalized synthetic images. The proposed model was trained primarily on COVID-19, pneumonia, and tuberculosis (TB) cases (n = 432,533 total augmented cases). Results: The proposed model is a lightweight and explainable model that assists with real-time detection, resulting in a better performance with an average accuracy of 97.93% with a standard deviation of 0.97%, average area under the curve (AUC) of 98.07%, average sensitivity of 98.46%, average specificity of 97.03%, average precision of 97.45%, and average F1 score of 97.95%. Conclusion: The proposed CNN model offers a validation and generalization capability for diverse image types in real-time. We conducted a comparative analysis of our model with the most advanced research. The integration of our approach with other clinical systems and internet of things (IoT) devices is feasible.

Acute ischemic stroke is a leading cause of death and disability worldwide, and accurate lesion segmentation is important for diagnosis and treatment planning. Current deep learning approaches for stroke-lesion segmentation often fail to use the inherent bilateral symmetry of the human brain and are thus missing a valuable anatomical prior that clinicians routinely use for lesion detection. This paper introduces SE-Net, a novel symmetry-enhanced neural network that systematically incorporates brain-symmetry priors at multiple levels within a deep learning framework. Our approach extends the 3D U-Net architecture with four specialized components: a symmetry-aware encoder with dual-stream processing, a symmetry difference module for highlighting pathological asymmetries, a perfusion-diffusion mismatch attention mechanism, and enhanced skip connections that preserve symmetry information throughout the network. Comprehensive evaluation using the Ischemic Stroke Lesion Segmentation (ISLES) 2022 dataset demonstrates exceptional performance with a Dice similarity coefficient of 96.72%, which represents an improvement of 13–39 percentage points over state-of-the-art methods. The Hausdorff distance was 14.21 mm, which indicates improved boundary precision by 35–66% improvement compared to previous approaches. Statistical analysis confirmed that highly significant performance gains were achieved (p < 0.001, Cohen’s d > 1.7). Qualitative analysis was performed using representative segmentation results, small lesion detection examples, and symmetry difference visualizations, and the results validated the clinical applicability of our approach. Cross-dataset evaluation demonstrates robust generalization with 86-89% zero-shot performance across different imaging protocols. Clinical validation through expert evaluation confirmed the method’s potential for practical translation with an inference time of 3.4 seconds, which is suitable for acute stroke protocols. SE-Net addresses critical challenges in stroke diagnosis while establishing a framework for incorporating domain-specific anatomical knowledge into deep learning architectures for medical imaging applications.