AI applications in prevalent diseases and disorders

Catania, Louis J.

doi:10.1016/b978-0-12-824477-7.00007-9

Cited by 6 publications

(1 citation statement)

References 224 publications

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“…With an intension of proposing a Rule based ML (RML) [10], the researchers again investigated upon 1100 instances fetched via ASDTest app with all toddler instances being dropped off. The behavior of eight ML classifiers: C4.5, Bagging, CART, RIDOR, RIPPER, Boosting, PRISM, Nnge [11] was compared with that of RML which showed the maximum efficacy among all.…”

Section: Literature Surveymentioning

confidence: 99%

ASD Classification in Adolescent and Adult Utilizing Deep Neural Network

Mohanty¹,

Parida²,

Patra³

2021

Atlantis Highlights in Computer Sciences

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Autism Spectrum Disorder (ASD) is one of the neurological illnesses affecting the behaviour and communicative skills of an individual. It hampers the recognition capability of an individual. Hence it is the primary responsibility towards the affected individuals with ASD for early detection to minimize its effect. ASD clinical diagnosis procedure is lengthy and expensive. So, against the procedure, ASD datasets are stored in authenticated sites like Kaggle and UCI Machine Learning (ML) repository to carry out clinical research. The data from all the category of individuals including adult, adolescent, child and toddler got collected by a mobile based ASDTest app with certain screening questions. The proposed method covered the category of adolescent and adult datasets with implementation of Landmark Isomap for dimension reduction and then improved Deep Neural Network prediction with classification (i-DNNPC) architecture for detecting ASD class. The evaluation of performance parameters confirmed the accomplishment of i-DNNPC classifier model.

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Section: Literature Surveymentioning

confidence: 99%