Enhancing Autism Detection through Multi-Algorithm Fusion

Machine learning based detection of autism can give significant help for improving early diagnosis and intervention approaches. In this paper, the authors build and test various machine learning models: Support Vector Machines (SVM), Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Decision Trees, and Logistic Regression. The dataset consists of important features like "A1_Score to A10_Score" and "Class/ASD." By using ensemble method -stacking accuracy improvements are made, with an accuracy of 87% reached with the stacked classifier. Contributing to this body of work literature, this paper proposes an effective approach of combining multiple algorithms for improved autism detection, enabling further developments for healthcare applications.