Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization.

摘要: With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers proposed tested, fact that a single classifier cannot effectively classify diagnose all diseases has almost accorded with. This seen number research attempts to arrive at consensus using ensemble classification techniques. this paper, hybrid system is ailments optimizing parameters for two techniques, namely, support vector (SVM) multilayer perceptron (MLP) technique. We employ three evolutionary algorithms optimize above, leading six alternative systems, also referred as (HISs). Multiple objectives, prediction accuracy, sensitivity, specificity, considered assess efficacy with existing ones. The model evaluated on 11 benchmark datasets, obtained results demonstrate our perform better in terms specificity. Pertinent statistical tests were carried out substantiate results.