applied to effectively diagnosis and prediction of dengue disease. The parameters setting of all approaches is illustrated in Table 3. The k-cross fold validation method is also used to validate the results of the proposed approach. Further, a confusion matrix is designed to evaluate the performance of PSO-ANN approach.Confusion matrix defines the relationship between value predicted by the model & actual value. The upper left cell of confusion matrix denotes the number of data classified as true while they are true (i.e. True positives) and the upper right cell denotes the number of data classified as true while they actually were false (i.e., false positive). The lower left cell denotes the number of data classified as false while they actually were true (i.e., false negatives) and lower right cell denotes the number of data classified as false while they were actually false (i.e. True, False). The performance of the PSO-ANN diagnostic model is measured using accuracy, sensitivity, specificity, error rate and area under curve (AUC) parameters. The results of the proposed model are also compared with other approaches like Naïve Bayes, Decision Tree, ANN and PSO.
\label{this-section-summarizes-the-results-of-our-study.-in-this-work-a-pso-ann-based-approach-is-applied-to-effectively-diagnosis-and-prediction-of-dengue-disease.-the-parameters-setting-of-all-approaches-is-illustrated-in-table-3.-the-k-cross-fold-validation-method-is-also-used-to-validate-the-results-of-the-proposed-approach.-further-a-confusion-matrix-is-designed-to-evaluate-the-performance-of-pso-ann-approach.confusion-matrix-defines-the-relationship-between-value-predicted-by-the-model-actual-value.-the-upper-left-cell-of-confusion-matrix-denotes-the-number-of-data-classified-as-true-while-they-are-true-i.e.-true-positives-and-the-upper-right-cell-denotes-the-number-of-data-classified-as-true-while-they-actually-were-false-i.e.-false-positive.-the-lower-left-cell-denotes-the-number-of-data-classified-as-false-while-they-actually-were-true-i.e.-false-negatives-and-lower-right-cell-denotes-the-number-of-data-classified-as-false-while-they-were-actually-false-i.e.-true-false.-the-performance-of-the-pso-ann-diagnostic-model-is-measured-using-accuracy-sensitivity-specificity-error-rate-and-area-under-curve-auc-parameters.-the-results-of-the-proposed-model-are-also-compared-with-other-approaches-like-nauxefve-bayes-decision-tree-ann-and-pso.}