Hybrid ML Algorithms for Learning Disability Forecast in School Going Children Using Python in Machine Learning Techniques

• Published in: 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA) pp. 1043 - 1049
• Main Authors: T, Margaret Mary, Prakash, V S, K S, Divya, George, Amalorpavam
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.11.2023
• Subjects: Classification algorithms; Learning Disability; Logistic regression; Machine learning algorithms; Markov chain model; Markov processes; Prediction algorithms; Predictive models; Random Forest; Random forests; Stochastic Gradient Descent; The K-fload cross validation and Hybrid classification
• DOI: 10.1109/ICECA58529.2023.10395259

A learning disability is a neurological illness that impairs a child's ability to read, speak, and do a variety of other skills. The World Health Organization (WHO) estimates that learning disabilities impact 15% of youngsters [14]. The most important challenge for researchers to perform in order to identify learning disabilities early on is efficient prediction and accurate categorization. Our primary goal in this effort is to use soft computing to create a model for the prediction and categorization of learning disabilities. This study proposes a hybrid approach for enabling classification in order to enhance the performance of prediction and classification. This method incorporated classification's primary five techniques. Random Forest, Logistic Regression, Stochastic Gradient Descent, and K-Fold cross validation. In order to implement the system used python. Results analysis reveals the predict of learning disability in effectively