Leveraging Machine Learning for Early Autism Detection via INDT-ASD
  Indian Database
Trapti Shrivastava Harshal Chaudhari Vrijendra Singh
Machine learning (ML) has advanced quickly, particularly throughout the area
of health care. The diagnosis of neurodevelopment problems using ML is a very
important area of healthcare. Autism spectrum disorder (ASD) is one of the
developmental disorders that is growing the fastest globally. The clinical
screening tests used to identify autistic symptoms are expensive and
time-consuming. But now that ML has been advanced, it's feasible to identify
autism early on. Previously, many different techniques have been used in
investigations. Still, none of them have produced the anticipated outcomes when
it comes to the capacity to predict autistic features utilizing a clinically
validated Indian ASD database. Therefore, this study aimed to develop a simple,
quick, and inexpensive technique for identifying ASD by using ML. Various
machine learning classifiers, including Adaboost (AB), Gradient Boost (GB),
Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Gaussian
Naive Bayes (GNB), Linear Discriminant Analysis (LDA), Quadratic Discriminant
Analysis (QDA), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM),
were used to develop the autism prediction model. The proposed method was
tested with records from the AIIMS Modified INDT-ASD (AMI) database, which were
collected through an application developed by AIIMS in Delhi, India. Feature
engineering has been applied to make the proposed solution easier than already
available solutions. Using the proposed model, we succeeded in predicting ASD
using a minimized set of 20 questions rather than the 28 questions presented in
AMI with promising accuracy. In a comparative evaluation, SVM emerged as the
superior model among others, with 100 $\pm$ 0.05\% accuracy, higher recall by
5.34\%, and improved accuracy by 2.22\%-6.67\% over RF. We have also introduced
a web-based solution supporting both Hindi and English.