%X For the last many years, dengue has been reported to be one of the main causes of death in Malaysia. For more than 40 years, Malaysia is suffering from this endemic problem. The mortality and morbidity are reported for the dengue cases in a higher number of conformed cases in Malaysia. As per statistics, 136,992 cases were reported from 2008 to 2012, the highest in the record. As per the report from the Ministry of Health (MOH) of Malaysia, 77 of cases are reported from the urban area and 23 from the rural area. Since much research have been carried out in history, many researchers had concluded their novel research work, but still dengue cases are not controlled. Hence, this research suggests a novel way to visualize dengue cases and the occurrence of cases. This research uses machine learning technology combined with (geographical information system) GIS to predict dengue cases in Malaysia. The area of research is limited to the Selangor state of Malaysia as this is the most vulnerable area for dengue cases. This research focuses on unsupervised learning techniques to predict the density of cases. K-mean, KNN, and Expectation-Maximization (EM) algorithms are used to cluster the cases and visualize the pattern of dengue spread. In conclusion, all these information are mapped on dynamic mapping which will give the exact coordinates where dengue can occur. Based on this location, the fogging team can be informed and can target a specific area. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
%K K-means clustering; Mapping; Maximum principle, Causes of death; Dynamic mapping; Expectation-maximization algorithms; Machine learning techniques; Machine learning technology; Research focus; Specific areas; Vulnerable area, Machine learning
%L scholars15702
%J EAI/Springer Innovations in Communication and Computing
%O cited By 0
%R 10.1007/978-3-030-66519-7₈
%D 2021
%A N. Mathur
%A V.S. Asirvadam
%A S.C. Dass
%A B.S. Gill
%I Springer Science and Business Media Deutschland GmbH
%T Dynamic Mapping and Visualizing Dengue Incidences in Malaysia Using Machine Learning Techniques
%P 195-226