Leveraging Machine Learning for Early Autism Detection via INDT-ASD Indian Database

TL;DR

The paper tackles the challenge of early ASD detection in India by leveraging ML on the AMI/INDT-ASD dataset to create a low-cost, efficient screening tool. It introduces an ensemble majority voting approach across CHS, RFE, and PCA to shrink the questionnaire from 28 to 20 items and evaluates a broad set of classifiers, with SVM delivering the best overall performance. A web-based, bilingual (Hindi/English) ASD screening tool is developed to translate these ML advances into practice, demonstrating high diagnostic potential against DSM-5 standards on Indian data. The work highlights the feasibility and impact of reducing screening burden while maintaining accuracy, and outlines avenues for enhancement through larger datasets and multimodal data integration.

Abstract

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.

Figures (8)

• Figure 1: Overview of Proposed Work Flow
• Figure 2: Performance Comparison of ML Models Across Various K Values
• Figure 3: Web-Based Application Screenshots
• Figure 4: Training Phase Performance Metrics results for fine-tuned ML Models
• Figure 5: Testing Phase Performance Metrics results for fine-tuned ML Models