Enhancing dengue diagnosis and surveillance by integrating machine learning technologies with the NS1 rapid test kit

BackgroundDengue has been a major health threat globally in recent years. In particular, dengue incidences continue to increase annually and the epidemic area has expanded primarily due to global warming. Therefore, effective case detection and surveillance strategies are crucial to tackle this global health challenge. In clinical practice, the rapid test kit detecting dengue non-structural protein 1 antigen and commonly referred as NS1, is widely employed for early diagnosis. However, real-world studies revealed that the sensitivity of the NS1 test kit ranged from approximately 61% to 95%. Since early diagnosis is really critical for disease surveillance in the early stage of a dengue epidemic, scientists have been working hard to develop novel diagnosis methods that can provide higher sensitivity levels. Methodology/Principal FindingsIn response to this challenge, in this study, we have developed a novel diagnosis procedure that integrates machine learning technologies with the NS1 test kit. Our experimental results revealed that we would be able to raise the sensitivity of the dengue diagnosis procedure to higher than 99% by incorporating machine learning based prediction models to screen the suspected patients with a negative NS1 result. Furthermore, the relative risks between the suspected patients who were predicted to be positive and those who were predicted to be negative exceeded 4.8. Conclusions/SignificanceThese results illustrate that the proposed approach provides an effective and efficient diagnosis procedure to address the global health challenge caused by spread of dengue. Author SummaryThis study has aimed to enhance surveillance of the dengue disease by integrating machine learning technologies with the rapid test kit commonly employed in early diagnosis. In clinical practice, the NS1 rapid test kit is widely employed for early diagnosis. However, real-world studies revealed that a certain percentage of the patients with a negative NS1 test result, ranging from 5% to 39%, were actually infected by dengue. Since early diagnosis is critical for disease control in the early stage of a dengue epidemic, scientists have been working hard to tackle this challenge. Based on this observation, this study was launched to investigate the effects of incorporating machine learning based prediction models to further screen those patients with a negative NS1 test result. The experimental results revealed that the proposed approach was able to identify over 99% of the patients who were infected by the dengue disease. Furthermore, the risk of the suspected patients who were predicted to be positive was 4.8 times higher than the risk of those who were predicted to be negative. The experimental results illustrate that the proposed approach provides an effective and efficient diagnosis procedure to enhance surveillance of the dengue disease.