Autistic Spectrum Disorder (ASD) is a neurological disease characterized by
difficulties with social interaction, communication, and repetitive activities.
The severity of these difficulties varies, and those with this diagnosis face
unique challenges. While its primary origin lies in genetics, identifying and
addressing it early can contribute to the enhancement of the condition. In
recent years, machine learning-driven intelligent diagnosis has emerged as a
supplement to conventional clinical approaches, aiming to address the potential
drawbacks of time-consuming and costly traditional methods. In this work, we
utilize different machine learning algorithms to find the most significant
traits responsible for ASD and to automate the diagnostic process. We study six
classification models to see which model works best to identify ASD and also
study five popular clustering methods to get a meaningful insight of these ASD
datasets. To find the best classifier for these binary datasets, we evaluate
the models using accuracy, precision, recall, specificity, F1-score, AUC, kappa
and log loss metrics. Our evaluation demonstrates that five out of the six
selected models perform exceptionally, achieving a 100% accuracy rate on the
ASD datasets when hyperparameters are meticulously tuned for each model. As
almost all classification models are able to get 100% accuracy, we become
interested in observing the underlying insights of the datasets by implementing
some popular clustering algorithms on these datasets. We calculate Normalized
Mutual Information (NMI), Adjusted Rand Index (ARI) & Silhouette Coefficient
(SC) metrics to select the best clustering models. Our evaluation finds that
spectral clustering outperforms all other benchmarking clustering models in
terms of NMI & ARI metrics and it also demonstrates comparability to the
optimal SC achieved by k-means.

利用不同机器学习算法研究自闭症的诊断方法，发现了最重要的特征并自动化了诊断过程，同时采用流行的聚类算法对数据集进行了进一步分析，得出了最佳聚类模型。