Optimized Random Forest for High-Accuracy Autism Spectrum Disorder Detection via Phenotypic Data

Autism Spectrum Disorder (ASD) is a mental disorder with a neurological condition noticed in patients by their persistent deficits in social communication and interaction, along with the possibility of the presence of repetitive motor behaviors or activities. Early diagnosis of this disorder is crucial for improving patients’ cognitive, emotional, and social development. Numerous studies on detecting autism exist; however, data limitations and imbalance affect their model generalization. This study proposes a new intelligent computational model for mental healthcare in individuals with ASD, utilizing machine learning (ML) to address these shortcomings. The proposed model enhances the random forest (RF) algorithm by setting optimal parameters and encompasses two key pipelines: 1) the data pipeline and 2) the learning pipeline. We first gathered a multi-source dataset, implemented integration and preprocessing via ML algorithms. The phenotypic data used were collected from 19 different sites and merged to ensure the diversity of the data used. The resulting dataset is subsequently fed into the learning pipeline, where a supervised ML algorithm is employed to create a trained computational model for detecting ASD. The model is based on tuning the RF algorithm by finding the optimal values for five key hyperparameters. After tuning the model, the accuracy of detecting ASD from phenotypic data reached 96.86%, with a sensitivity of 97.14% and a false positive rate of 3.39%. Comparing the tuned RF model with different ML models verified that tuning and optimizing RF achieves a preeminent classification accuracy for ASD detection using phenotypic data, as the accuracy of RF without tuning is 95.06%. In addition, to validate the tuned RF model’s real-world applicability, a separate qualitative study was conducted on five independent, narrative-based case studies, where the model accurately classified four of them by translating descriptive language into quantitative features.