A Machine Learning Approach For Dengue Disease Prediction

Abstract

The fact that dengue fever is a global health threat, that millions of cases are recordedevery year, is a matter of urgency. The prompt and accurate identification of this virus, which is transmitted by mosquitoes, is a prerequisite for treatment and control. Theexperts predict 400 million cases and 25,000 deaths in the process. It is a major publichealth problem a year. Machine learning algorithms, which have recently proven tobeaviable and promising tool in medical diagnostic, can be a possible method of diagnosingdengue fever. In this work, a machine learning-based method of diagnosing dengue fever using the numerical data given by the patients is brought to life. Dengue fever, amosquito-borne illness, is a tropical and subtropical disease that is native to these regions. The first step is the diagnosis and treatment of the disease as soon as possible in order toprevent the occurrence of the major sequelae and the death caused by dengue fever. Machine learning and artificial intelligence are applied for the purpose of datainterpretation and prediction. It has been verified as a successful tool in the detectionof dengue fever from a broad range of data sources, including blood samples, medical records, and environmental data. This study provides a machine learning solutionfor dengue fever diagnosis. Our method is based on a fusion of various factors that we import from clinical records and blood samples to design a classifier. We evaluated our randomforest method on a dataset made up of 820 patients and we proved that it can reach 98%accuracy. This paper shows the possible use of machine learning in the early diagnosis of dengue infection. The improvement of the diagnosis and treatment of dengue fever, this may eventually lead to a decrease in the death rate of this disease. The current methods of diagnosing dengue fever are mainly expensive, slow and not always accurate. Thanks tothe ML of the diagnostic systems for dengue fever, we can now create themthat will bemore accurate and efficient. Machine learning algorithms can be taught using the hugeamount of clinical and image data which are related to dengue fever, and thus, theycanbe used to identify the patterns of dengue fever.