PSO-ANN based diagnostic model for the early detection of dengue disease

Large numbers of machine learning approaches have been developed for analysis of medical data in recent years. These approaches have also proved their significance through accurate and earlier diagnosis of diseases. The objective of this work is to develop a diagnostic model for earlier diagnosis of dengue disease. Dengue fever is spread through the bite of the female mosquito (Aedes aegypti). The symptoms of this fever are similar to other fever such as that of Viral influenza, Chikungunya, Zika fever, and so on. However, in this fever, human life can be at risk due to severe depletion of blood platelets. Therefore, early diagnosis of dengue disease can help in protecting human lives by making a preventive move before it turns into an infectious disease. In this work, an effort is made to develop a PSO-ANN based diagnostic model for earlier diagnosis of dengue fever. In the proposed model, PSO technique is applied to optimize the weight and bias parameters of ANN method. Further, PSO optimized ANN approach is used to detect dengue patients. The effectiveness of the proposed model is evaluated based on accuracy, sensitivity, specificity, error rate and AUC parameters. The results of the proposed model have been compared with other existing approaches like ANN, DT, NB, and PSO. It is observed that the proposed diagnostic model is a proficient and powerful model for more accurate and earlier detection of dengue fever.