Research Area: Machine Learning
Autism Spectrum Disorder (ASD) is a neurological disorder which might have a lifelong impact on the language learning, speech, cognitive, and social skills of an individual. Its symptoms usually show up in the developmental stages, i.e., within the first two years after birth, and it impacts around 1% of the population globally [https://www.autism-society.org/whatis/facts-and-statistics/. Accessed 25 Dec 2019]. ASD is mainly caused by genetics or by environmental factors; however, its conditions can be improved by detecting and treating it at earlier stages. In the current times, clinical standardized tests are the only methods which are being used, to diagnose ASD. This not only requires prolonged diagnostic time but also faces a steep increase in medical costs. To improve the precision and time required for diagnosis, machine learning techniques are being used to complement the conventional methods. We have applied models such as Support Vector Machines (SVM), Random Forest Classifier (RFC), Naïve Bayes (NB), Logistic Regression (LR), and KNN to our dataset and constructed predictive models based on the outcome. The main objective of our paper is to thus determine if the child is susceptible to ASD in its nascent stages, which would help streamline the diagnosis process. Based on our results, Logistic Regression gives the highest accuracy for our selected dataset.
Keywords:
Autism spectrum disorder
Machine learning
Dataset
Preprocessing
Encoding
SVM
KNN
Random forest
Logistic regression
Confusion matrix
Precision
Recall
F1 score
Accuracy
Author(s) Name: Kaushik Vakadkar, Diya Purkayastha & Deepa Krishnan
Journal name: SN Computer Science
Conferrence name:
Publisher name: Springer
DOI: 10.1007/s42979-021-00776-5
Volume Information: 2
