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Enhancing autism severity prediction: A fusion of convolutional neural networks and random forest model

2024, vol.16 , no.2, pp. 51-62

Article [2024-02-05]

Authors
R. Ramya
S. Panneer Arokiaraj
Abstract

A neurological condition affecting both the brain and behaviour is identified as autism spectrum disorder (ASD). Due to the absence of a reliable medical test for detecting autism, diagnoses rely on historical evidence. Essential in assessing the degree of autism are models like Convolutional Neural Networks (CNNs) and Random Forest (RF). In order to reduce the number of diagnostic tests required for autism diagnosis, this research work presents a new hybrid model that combines the strengths of RF and CNNs, providing healthcare solutions. It is noteworthy that this model properly predicted the severity of autism with an astounding accuracy rate of 99.15% when applied to historical data gathered from the Kaggle Repository.

Keywords

Autism Spectrum Disorder (ASD), Data Science models, Random Forest and Convolutional Neural Networks (CNN-RF) Model, Accuracy, Precision, Recall, F1-Score and Kappa Statistic

DOI

https://doi.org/10.59035/VNWF2548

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Citation of this article:

R. Ramya, S. Panneer Arokiaraj. Enhancing autism severity prediction: A fusion of convolutional neural networks and random forest model. International Journal on Information Technologies and Security, vol.16 , no.2, 2024, pp. 51-62. https://doi.org/10.59035/VNWF2548