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Abstract

Dengue the global problem is common in more than 110 countries. Dengue fever is a vector borne disease caused by the female Aides Egypt and Aides Albopictus mosquitoes which adapt well to human environments. Dengue disease can cause severe damages to the society. Hence, it is critical to predict a dengue disease in advance to minimize the damage and loss caused by the disease. By keeping this voluminous data we can predict the future occurrences of the disease earlier and safe guard the people. The collected dataset was experimented with Weak and Net Beans IDE and preprocessed to remove missing values in multi-view dataset, feature selection is done and classification is done effectively with Support Vector Machine and Sequential minimal optimization applied in this research for predicting dengue disease.

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How to Cite
C. Santhosh, S.Saranya, K. Harini, & T.Dharshini. (2021). Predictive models of missing data in multi-view dataset. International Journal of Intellectual Advancements and Research in Engineering Computations, 7(1), 1092–1096. Retrieved from https://ijiarec.com/ijiarec/article/view/194