active ARFF Public Domain (CC0) Visibility: public Uploaded 12-05-2021 by Meilina Reksoprodjo
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Autistic Spectrum Disorder (ASD) is a neurodevelopment condition associated with significant healthcare costs, and early diagnosis can significantly reduce these. Unfortunately, waiting times for an ASD diagnosis are lengthy and procedures are not cost effective. The economic impact of autism and the increase in the number of ASD cases across the world reveals an urgent need for the development of easily implemented and effective screening methods. Therefore, a time-efficient and accessible ASD screening is imminent to help health professionals and inform individuals whether they should pursue formal clinical diagnosis. The rapid growth in the number of ASD cases worldwide necessitates datasets related to behaviour traits. However, such datasets are rare making it difficult to perform thorough analyses to improve the efficiency, sensitivity, specificity and predictive accuracy of the ASD screening process. Presently, very limited autism datasets associated with clinical or screening are available and most of them are genetic in nature. Hence, we propose a new dataset related to autism screening of adults that contained 20 features to be utilised for further analysis especially in determining influential autistic traits and improving the classification of ASD cases. In this dataset, we record ten behavioural features (AQ-10-Adult) plus ten individuals characteristics that have proved to be effective in detecting the ASD cases from controls in behaviour science.

21 features

A1_Scorenominal2 unique values
0 missing
A2_Scorenominal2 unique values
0 missing
A3_Scorenominal2 unique values
0 missing
A4_Scorenominal2 unique values
0 missing
A5_Scorenominal2 unique values
0 missing
A6_Scorenominal2 unique values
0 missing
A7_Scorenominal2 unique values
0 missing
A8_Scorenominal2 unique values
0 missing
A9_Scorenominal2 unique values
0 missing
A10_Scorenominal2 unique values
0 missing
agenumeric46 unique values
2 missing
gendernominal2 unique values
0 missing
ethnicitynominal11 unique values
95 missing
jundicenominal2 unique values
0 missing
austimnominal2 unique values
0 missing
contry_of_resnominal67 unique values
0 missing
used_app_beforenominal2 unique values
0 missing
resultnumeric11 unique values
0 missing
age_descnominal1 unique values
0 missing
relationnominal5 unique values
95 missing
Class/ASDnominal2 unique values
0 missing

19 properties

Number of instances (rows) of the dataset.
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
Number of missing values in the dataset.
Number of instances with at least one value missing.
Number of numeric attributes.
Number of nominal attributes.
Percentage of instances having missing values.
Average class difference between consecutive instances.
Percentage of missing values.
Number of attributes divided by the number of instances.
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
Number of binary attributes.
Percentage of binary attributes.

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