UChOA‐ANN based effective diagnostic model for early detection and accurate prediction of dengue affected blood samples

• Published in: Concurrency and computation Vol. 35; no. 8
• Main Authors: Ramakrishna, Malige Mahadevaiah, Karthikeyan, Vedanandam
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 10.04.2023; Wiley Subscription Services, Inc
• Subjects: Accuracy; Algorithms; Artificial Neural Network; Artificial neural networks; Blood; blood samples; dengue; Dengue fever; Diagnosis; diagnostic model; Diagnostic systems; Disease; Infectious diseases; Optimization; Sensitivity; Signs and symptoms; Updated Chimp Optimization Algorithm; Viral diseases
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.7597

Summary Dengue virus infection is one of the major worldwide health issues and a substantial epidemic infectious human disease. More than 2 billion humans live in dengue susceptible regions with an annual infection mortality rate is about 5%–20%. At the initial stages, it is difficult to differentiate dengue fever symptoms from other similar diseases. Therefore, early diagnosis of dengue disease can help in protecting human lives by making a preventive move before it turns into an infectious disease. Although early detection is critical to reducing dengue disease deaths, accurate dengue diagnosis requires a long time due to the numerous clinical examinations. For this, several techniques have been presented. But, the accuracy of these methods is not enough. Thus, this issue necessitates the development of a new diagnostic schema. Hence, to identify the dengue‐affected blood samples effectively, Updated Chimp Optimization Algorithm‐Artificial Neural Network (UChOA‐ANN) based diagnostic model is proposed in this paper. UChOA‐ANN‐based diagnostic model has been implemented and validated for the early detection and accurate prediction of dengue disease. Here, to improve the performance of ANN, UChOA is used for selecting the weight parameters of ANN optimally. The results show that the proposed diagnostic model has achieved maximum sensitivity, specificity, and accuracy at 92%, 96%, and 94%, respectively. In addition, the results reveal that the proposed diagnostic model has outperformed other methods for detecting dengue‐affected blood samples in terms of sensitivity, specificity, accuracy, PPV, NPV, FPR, FNR, and FDR. Our proposed diagnostic model has been capable to diagnose dengue in assisting doctors in its early stage with high accuracy.