Abstract
Dengue is the most common viral fever for the people. This is also known as life-threatening disease. Dengue has become more and more evident this year in Bangladesh. It has taken the lives of many in our country. And the number of dengue fever patients is increasing day by day. There are many people at risk from dengue. Early forecast of dengue can spare individual’s life by cautioning them to take legitimate conclusion and care. But it is difficult to say in advance whether this will happen or not. The aim of this piece of research work is to analysis the symptoms of dengue fever and early prediction of the symptoms that can be seen in years ahead. For predicting the symptoms, two different machine learning algorithms have been used. Support vector machine (SVM) and random forest classifier algorithm have been used. Finally, the accuracy of these two has been evaluated and the confusion matrix has been shown. And then, we have talked about the algorithm which is better for our dataset.
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Dourjoy, S.M.K., Rafi, A.M.G.R., Tumpa, Z.N., Saifuzzaman, M. (2021). A Comparative Study on Prediction of Dengue Fever Using Machine Learning Algorithm. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_49
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DOI: https://doi.org/10.1007/978-981-15-4218-3_49
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