MAPLE: A Novel Processing Technique for Adult Autism Prediction - Rajam, Francis Julee; Swakkin, Britto Ramesh Kumar (Engineering, Technology & Applied Science Research, 2025)
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects individuals throughout their lives. Predicting autism in adults is a critical challenge with significant implications for early intervention and support. Current autism prediction systems in adults frequently encounter difficulties arising from incomplete or noisy data, which can affect the accuracy of prediction. This paper presents MAPLE (Missing data imputation and Anomaly removal for Preprocessing with Learning Enhancement), a novel algorithm designed to address these challenges and improve data quality in existing approaches. Existing systems for the prediction of autism in adults often struggle with incomplete data and noise, which can compromise the accuracy of the predictions. MAPLE addresses these issues by incorporating two key algorithms: MARVEL and SAFARI. MARVEL, the Multi-model Approach for Regression-based Value Estimation of Lost data, efficiently imputes missing values by leveraging diverse regression models. This technique ensures robust imputations even in incomplete data, contributing to more accurate predictions. SAFARI, the Statistical Anomaly Filter with Automated Range Identification, identifies and removes outliers from the imputed dataset, enhancing the model’s robustness and generalization capabilities and, as a result, improving data quality. Comprehensive experiments were carried out using a real-world autism prediction dataset for adults to evaluate the performance of MAPLE, focusing on improving data quality. The results demonstrate that MAPLE outperforms existing systems, achieving significantly higher prediction accuracy while effectively addressing data quality challenges. This improvement is crucial for early diagnosis and intervention, ultimately enhancing the quality of life for individuals with autism.