Optimizing Autism Spectrum Disorder Identification with Dimensionality Reduction Technique and K-Medoid

This research addresses the challenges of diagnosing and treating Autism Spectrum Disorder (ASD) using dimensionality reduction techniques and machine learning approaches. Challenges in social interaction, communication, and repetitive behaviours characterize ASD. The dimension reduction used in this research aims to identify what features influence autism cases. Several dimension data reduction techniques used in this research include PCA, Isomap, t-SNE, LLE, and factor analysis, using metrics such as Purity, silhouette score, and the Fowlkes-Mallows index. The machine learning approach applied in this study is k-medoid. By employing this method, our goal is to pinpoint the unique characteristics of autism that may facilitate the detection and diagnosis process. The data used in this research is a dataset collected for autism screening in adults. This dataset contains 20 features: ten behavioural features (AQ-10-Adult) and ten individual characteristics. The results indicate that Factor Analysis outperforms other methods based on purity metrics. However, due to data structure issues, the t-SNE method cannot be evaluated using purity metrics. PCA and LLE consistently provide stable silhouette scores across different values. The Fowlkes-Mallows index results closely align, but t-SNE tends to yield lower values. The choice of algorithm requires careful consideration of preferred metrics and data characteristics. Factor analysis is adequate for Purity, while PCA and LLE consistently perform well. This research aims to improve the accuracy of ASD identification, thereby enhancing diagnostic and treatment precision.