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

**Authors:** Trapti Shrivastava, Harshal Chaudhari, Vrijendra Singh  
**arXiv:** [2404.02181](https://arxiv.org/abs/2404.02181)  
**Date:** 2024-04-02  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.02181v1/

## Key points

- Machine learning has revolutionized healthcare, especially in diagnosing neurodevelopmental disorders like Autism Spectrum Disorder (ASD).
- Early identification of ASD is crucial for effective intervention.
- Traditional clinical screening tests for ASD are expensive and time-consuming.
- Machine learning techniques have made it possible to identify ASD at an early stage.
- A study used various machine learning classifiers to develop a predictive model for autism using a clinically validated Indian ASD database.
- The workflow included phases such as Data Pre-Processing, Feature Optimization, Machine Learning Classifiers training for Best Value of K, Parameter Optimization of ML Classifiers, and Model Performance Analysis.
- The SVM model was the superior classifier with 100% accuracy and higher recall compared to other models.
- This refined methodology can improve early detection and intervention strategies for individuals with ASD.

## Summary

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Machine learning has revolutionized the healthcare industry, particularly in the diagnosis of neurodevelopmental disorders such as Autism Spectrum Disorder (ASD). Early identification of ASD is crucial for effective intervention, and traditional clinical screening tests are expensive and time-consuming. However, with the advancement of machine learning techniques, it has become feasible to identify ASD at an early stage. In this study, various machine learning classifiers were utilized to develop a predictive model for autism using a clinically validated Indian ASD database. The proposed workflow consisted of five main phases: Data Pre-Processing, Feature Optimization, Machine Learning Classifiers training for Best Value of K, Parameter Optimization of ML Classifiers, and Model Performance Analysis. The SVM model emerged as the superior classifier with 100% accuracy and higher recall compared to other models. This refined methodology provides valuable insights into improving early detection and intervention strategies for individuals with ASD.