Abstract: Timely diagnosis of diseases is considered vital for treatment. So far, many methods have been created for this purpose; autism spectrum disorder is one of these diseases. In this disease, developmental and developmental disorders will accompany the patient especially since childhood. The range of this disease is wide and it can usually be diagnosed by performing a series of clinical tests. Usually, the symptoms of this disease appear in childhood and before the age of three, and they differ according to the severity of the disease and its symptoms, which may appear in some cases from 5 months of age to two years of age. One of the important and debatable points is the timely diagnosis of this disease in adults, which unfortunately was not diagnosed in childhood, and this causes a series of behavioral problems in the social life of people with autism, and the person, by referring to a neurologist and Nerves and performing a series of clinical evaluations are known to be suspicious of autism spectrum disorder. Therefore, it is important to provide methods that can identify the relationship between different characteristics in contracting this disease. In this research, an attempt is made to predict the effect of each of the parameters on the diagnosis of the disease and also the process of the disease by using Bayes' law and genetic algorithm. In this method, mutual validation technique is used to optimize input and output data. First, the data are pre-processed and in the next step they are classified by Naive Bayes (kernel) which achieves 91% accuracy and then they are optimized with genetic algorithm which reaches 94.06% accuracy. Also, the data were tested with decision tree and Naive Bayes algorithms, and their results were compared.
Keywords: autism, Bayes law, Naive Bayes, Naive Bayes (Kernel) genetic algorithm, cross validation
Cite:
Mohammadali Mohammadi, "Diagnosis of Autism Spectrum Disorder in Adults by Combining Bayes' Law and Genetic Algorithm", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 4, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.134104.
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DOI:
10.17148/IJARCCE.2024.134104
