Abstract
Dengue fever (DF) and the potentially fatal dengue haemorrhagic fever (DHF) are continue to be a crucial public health concern in Malaysia. This paper proposes a prediction model that incorporates Support Vector Machine (SVM) in predicting future dengue outbreak. Datasets used in the undertaken study includes data on dengue cases provided by the Health Department in Kelantan, Malaysia. Data scaling were applied to normalize the range of features before being fed into the training model. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear function. The SVM with RBF kernel function was superior to the other techniques because it obtains the highest prediction accuracy of 85%. The polynomial is an alternative model that can achieve a high prediction performance in terms of sensitivity (76%) and specificity (87%).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
