Auto-calibration of Support Vector Machines for detecting disease outbreaks

Support Vector Machines (SVM) have several tuning parameters such as the kernel function type. This work proposes to develop an algorithm to calibrate the SVM automatically for detecting disease outbreaks based on Telehealth data. Two sets of simulated data are generated based on real Telehealth calls and an outbreak profile. The Telehealth data is related to respiratory disease syndrome. The outbreak profile is created based on real outbreak data. The first data set is used by the SVM to model the relation between call counts and the occurrence of a respiratory outbreak; however, the other data set is used for testing the resulting model. This model is auto-calibrated by optimizing four parameters using a Genetic Algorithm. These parameters are the tradeoff between the training error and the margin of the classifying hyperplane, kernel function type used, the hyperplane type used and the threshold level at which the occurrence of an outbreak is detected.