Modified Grasshopper Optimization Algorithm for detection of Autism Spectrum Disorder
Physical Communication(2020)
摘要
Autism Spectrum Disorder (ASD) is a disorder of neurodevelopment whose delayed diagnosis has been posing a barrier in alleviating the severity of the conditions of the sufferers. ASD patients experience difficulties with social communication and interaction. They also show restricted and repetitive patterns of behavior. Approximately 62 million people are diagnosed with ASD globally. Males are about 3–4 times more likely to suffer from ASD than females. Statistically, ASD can be detected between the age of one to two, but some cases may remain undetected for a substantial period. It is crucial to detect ASD precisely and at the nascent stage to remediate the disease. The presented paper proposes an algorithm, namely Modified Grasshopper Optimization Algorithm (MGOA), capable of detecting Autism Spectrum Disorder at all stages of life. GOA is a nature-inspired algorithm that has the potential to explore and exploit the search space effectively. Through this paper, we have attempted to overcome the shortcomings of the traditional GOA, resulting in early diagnosis of the disease. The algorithm is used on the three ASD screening datasets targeting different age groups, namely children, adolescents, and adults, are used for numerical experimentation, and the results are contrasted with the state-of-the-art algorithms. The proposed algorithm with the Random Forest classifier predicted ASD with an approximate accuracy of 100% with specificity and sensitivity as 100% at all stages of life.
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关键词
Autism Spectrum Disorders,Grasshopper Optimization Algorithm,Random Forest,Machine learning
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