A New Hybrid Classification Framework in Childhoods Allergies with Dataset Slicing Method

Abstract

Childhood allergies, particularly food allergies, are growing more frequent. Their major influence on children's health and well-being has piqued the interest of worldwide public health officials. The increased prevalence of childhood allergies in Turkey, where these patterns are also relevant, adds urgency to the need for effective classification and management options. This study addresses the shortcomings of simple classification algorithms in obtaining high accuracy by presenting a novel hybrid classification methodology. The research creates a novel method where three different prediction models are built by combining Support Vector Machine and Decision Tree classifiers. This method improves the classification process by taking into account instances that have been incorrectly classified as possible sources of useful information instead of just being noise. This instance filtering-based hybrid classification algorithm that is used in this study maintains the simplicity of interpreting learning outcomes while achieving comparatively high accuracy. Extensive experiments on the allergy dataset show the effectiveness of this hybrid approach, with an impressive accuracy of 0.906. This greatly outperforms the fundamental classification algorithms. The experimental outputs have important implications for medical professionals. This study might add a valuable contribution to the literature by giving a fresh solution to childhood allergy classification.