Modified Grasshopper Optimization Algorithm for detection of Autism Spectrum Disorder

Goel, Nikita; Grover, Bhavya; Anuj, .; Gupta, Deepak; Khanna, Ashish; Sharma, Moolchand

doi:10.1016/j.phycom.2020.101115

Cited by 31 publications

(26 citation statements)

References 19 publications

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“…As shown in Table 2 , the commonly implemented ML algorithms are Random Forest (RF) [ 12 , 43 , 47 , 51 ], Support Vector Machines (SVM) [ 37 , 38 , 40 , 49 , 50 ], Alternative Decision Tree (ADTree) [ 34 , 35 , 39 , 45 ], and Logistic Regression (LR) [ 13 , 37 , 48 ]. To achieve comparative results, most of the studies employed several algorithms, such as Adaboost, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naïve Bayes, and K-Nearest Neighbor (KNN).…”

Section: Resultsmentioning

confidence: 99%

“…The most utilized data collection instruments are AQ-10 [ 11 , 13 , 43 , 44 , 46 , 47 , 48 , 49 , 51 , 52 ], Q-CHAT-10 [ 11 , 44 , 46 , 52 ], ADOS [ 34 , 37 , 39 , 40 , 42 , 50 ], ADI-R [ 35 , 38 , 42 ], and Social Responsiveness Scale (SRS) [ 36 , 38 , 53 ]. Others include Autism Behavior Checklist, Aberrant Behavior Checklist, Clinical Global Impression [ 45 ], and MCHAT-based Pictorial Autism Assessment Schedule (PASS) [ 12 ].…”

Section: Resultsmentioning

confidence: 99%

“…The most prominent sources of data utilized in the studies include Boston Autism Consortium (AC), Autism Genetic Resource Exchange (AGRE), Simons Simplex Collection (SSC) [ 34 , 35 , 36 , 37 , 39 , 50 , 53 ], National Database for Autism Research (NDAR) [ 37 , 39 ], and Simons Variation In Individuals Project (SVIP) [ 37 , 39 , 50 ]. Other studies utilized data sets from ASDTest: Kaggle and UCI ML repository [ 11 , 13 , 43 , 44 , 46 , 47 , 48 , 49 , 51 , 52 ], Association of Parents and Friends for the Support and Defense of the rights of people with Autism (APADA) [ 42 ], PASS app [ 12 ], Ondokuz Mayis University Samsun [ 45 ], and ASD outpatient clinics in Germany [ 40 ]. To achieve standardized comparative results, there is a need for standardized ASD data repositories for machine learning studies [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

“…Apart from the common aim of streamlining the various data collection instruments followed by model evaluation, other studies focused on either optimizing the machine-learning algorithms [ 49 , 51 ], proposing input optimization techniques [ 43 , 44 , 46 , 47 ], or implementing ML-based screening apps [ 11 , 12 ]. For instance, Goel et al [ 51 ] proposed Modified Grasshopper Optimization Algorithm (MGOA) for improved performance over common ML algorithms. The proposed MGOA (GOA with Random Forest classifier) outperformed other basic models and predicted ASD with approximate accuracy, specificity, and sensitivity of 100%.…”

Section: Resultsmentioning

confidence: 99%

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A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder

Çavuş

Lawan

Ibrahim

et al. 2021

JPM

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Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the world. Several studies have utilized behavioral data in developing and evaluating the performance of machine learning (ML) models toward quick and intelligent ASD assessment systems. However, despite the good evaluation metrics achieved by the ML models, there is not enough evidence on the readiness of the models for clinical use. Specifically, none of the existing studies reported the real-life application of the ML-based models. This might be related to numerous challenges associated with the data-centric techniques utilized and their misalignment with the conceptual basis upon which professionals diagnose ASD. The present work systematically reviewed recent articles on the application of ML in the behavioral assessment of ASD, and highlighted common challenges in the studies, and proposed vital considerations for real-life implementation of ML-based ASD screening and diagnostic systems. This review will serve as a guide for researchers, neuropsychiatrists, psychologists, and relevant stakeholders on the advances in ASD screening and diagnosis using ML.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder

Çavuş

Lawan

Ibrahim

et al. 2021

JPM

View full text Add to dashboard Cite

show abstract

“…Goel et al [120] developed a modified GOA by combining GOA with the random forest approach for Autism Spectrum This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Section: ) Other Improved Grasshopper Optimization Algorithmmentioning

confidence: 99%

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

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Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

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Adaptive autism behavior prediction using improved binary whale optimization technique

Ramachandran¹,

Martin²

2022

Concurrency and Computation

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Summary Autism spectrum disorder (ASD), a neuro developmental disorder is a bottleneck to several clinical researchers owing to the data modularization, subjective analysis and shifts in the accurate prediction of the disorder among the sample population. Subjective clinical analysis suffers from lengthy procedure which is a time‐consuming process. The present research focuses on the prediction of ASD disorder using improved binary whale optimization that provides accuracy in the feature selection for the contribution towards the disorder and improves the accuracy in decision making of predicting the presence of disorder. The proposed technique is carried out in two steps: the acute features contributing to the disorder is selected using the binary whale optimization method and the optimal feature is subjected to that render decision of predicting the presence of ASD. The state‐of‐the‐art disorder dataset is tested with conventional techniques like particle swarm optimization (PSO), genetic algorithm (GA), particle swarm optimization with genetic algorithm (PSO‐GA), whale optimization method and binary whale optimization method. Based on the results, the improved binary whale optimization method is proposed and validates the effectiveness of the deciding the autism spectrum disorder.

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Modified Grasshopper Optimization Algorithm for detection of Autism Spectrum Disorder

Cited by 31 publications

References 19 publications

A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder

A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Adaptive autism behavior prediction using improved binary whale optimization technique

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