Learning Disability Detection and Classification Analysis Between Two Classification Algorithms

This research study presents the analysis of learning disability detection and classification using two classification algorithms. Learning disabilities are neurodevelopmental disorders that affect the ability to acquire and process information. Detecting and classifying these disabilities accurately is crucial for providing appropriate interventions and support to affected individuals. This research analyzes two categorization algorithms: Random Forest (RF) and Support Vector Machine (SVM). Although RF is an ensemble learning method that incorporates several decision trees to produce predictions, SVM is a supervised machine learning algorithm that employs hyperplanes to split data points into distinct groups. This analysis compares the two algorithms' performance in terms of F1-score, recall, accuracy, and precision. A dataset consisting of features related to cognitive abilities, academic performance, and behavioral characteristics of individuals with and without learning disabilities is used for the analysis. The results show that both SVM and RF achieve high accuracy in learning disability detection and classification. However, SVM performs better than RF because it has greater precision and recall. This indicates that SVM is better at correctly identifying individuals with learning disabilities and minimizing false positives and false negatives. These algorithms can assist in early identification and intervention, enabling targeted support for individuals with learning disabilities.