ASD-Predict: A Multi-class Explainable Autism Disorder Severity Prediction Using LightGBM and SMOTE

Timely assessment of autism disorder (ASD) severity
is significant for proper disease diagnosis and healthcare of a
person or child. While a sizable amount of machine learning
research has focused on autism screening, most of this work uses
a limited number of classifiers and seldom offers a methodical
comparison of learning (ML and DL) techniques for multiple
type of autism disorder severity assessment. In order to evaluate
autism severity across four levels, this study introduces ASDPredict, an adaptive learning framework that uses machine
and deep learning-based methods. To improve data quality and
model dependability, the framework employs comprehensive data
preparation techniques with encoding and feature scaling. Seven
predictive machine learning models (RF, XGBoost, LightGBM,
MLP, RNN, ANN and LSTM) are developed and assessed.
Stratified cross-validation, proper feature selection, and parameter tuning are conducted along with explainability results. The
experimental results highlighted that the suggested LGBM-based
framework offers an accuracy of 98.09 percent and outperforms
the traditional background work’s performance.