A Hybrid Machine Learning Model for Accurate Autism Diagnosis

Abstract

The healthcare industry faces significant challenges in managing and processing large volumes of unstructured, real-time medical data. As such, there is a growing need for advanced techniques to handle complex data in the diagnosis of disorders like Autism Spectrum Disorder (ASD). This study presents a Big Data and Machine Learning-based Medical Data Classification (BDML-MDCASD) model aimed at improving the accuracy and efficiency of ASD diagnosis. The proposed model employs an improved Squirrel Search Algorithm-based Feature Selection (ISSA-FS) to identify the most relevant features from medical data. Additionally, a hybrid classification approach is introduced, combining Autoencoder (AE) with the Butterfly Optimization Algorithm (BOA) to enhance detection accuracy. To manage and process large datasets effectively, the MapReduce tool is used for efficient data handling. The model was evaluated across multiple ASD datasets, including ASD-Children (292 instances), ASD-Adolescent (104 instances), and ASD-Adult (704 instances). Simulation results demonstrate that the BDML-MDCASD model outperforms traditional methods, achieving a classification accuracy of 92%, precision of 90%, and recall of 93%. These results underscore the potential of the proposed model in providing a robust, automated solution for early ASD detection, offering a significant advancement over existing diagnostic methods.