The tool can identify symptoms of dengue, malaria, leptospirosis, and scrub typhus.
the world's population living in areas where they are at risk of contracting the disease.
The need for a tool that could aid in identifying the early symptoms and distinguishing laboratory parameters of these infections is imperative in reducing their prevalence. While the first part of the questionnaire comprised six questions that were diseased specific - such as the frequency of different tropical infections, the number of cases treated in a week, obstacles in treating tropical infections, challenges in the management of infections, and the perceived need for tool development.
WEKA is an open-source machine learning software in JAVA. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization. The tool offered a predictability of 60.7 percent, 62.5 percent, and 66 percent for dengue, malaria, and leptospirosis, respectively, and a 38 percent predictability for scrub typhus.
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