{ "data_id": "43414", "name": "Autism-Screening", "exact_name": "Autism-Screening", "version": 1, "version_label": "v1.0", "description": "Data Set Name: Autistic Spectrum Disorder Screening Data for Adult\nAutistic 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. \nSource:\nFadi Fayez Thabtah\nDepartment of Digital Technology\nManukau Institute of Technology,\nAuckland, New Zealand\nfadi.fayezmanukau.ac.nz\nData Type: Multivariate OR Univariate OR Sequential OR Time-Series OR Text OR Domain-Theory\nNominal \/ categorical, binary and continuous \nTask: Classification\nAttribute Type: Categorical, continuous and binary \nArea: Medical, health and social science\nFormat Type: Non-Matrix\nNumber of Instances (records in your data set): 704\nNumber of Attributes (fields within each record): 21\nRelevant Information: For Further information about the attributes\/feature see below table.\nAttribute Information:\nAttribute Type Description \nAge Number Age in years \nGender String Male or Female \nEthnicity String List of common ethnicities in text format \nBorn with jaundice Boolean (yes or no) Whether the case was born with jaundice\nFamily member with PDD Boolean (yes or no) Whether any immediate family member has a PDD \nWho is completing the test String Parent, self, caregiver, medical staff, clinician ,etc.\nCountry of residence String List of countries in text format\nUsed the screening app before Boolean (yes or no) Whether the user has used a screening app\nScreening Method Type Integer (0,1,2,3) The type of screening methods chosen based on age category (0=toddler, 1=child, 2= adolescent, 3= adult)\nQuestion 1 Answer Binary (0, 1) The answer code of the question based on the screening method used \nQuestion 2 Answer Binary (0, 1) The answer code of the question based on the screening method used \nQuestion 3 Answer Binary (0, 1) The answer code of the question based on the screening method used \nQuestion 4 Answer Binary (0, 1) The answer code of the question based on the screening method used \nQuestion 5 Answer Binary (0, 1) The answer code of the question based on the screening method used \nQuestion 6 Answer Binary (0, 1) The answer code of the question based on the screening method used \nQuestion 7 Answer Binary (0, 1) The answer code of the question based on the screening method used \nQuestion 8 Answer Binary (0, 1) The answer code of the question based on the screening method used \nQuestion 9 Answer Binary (0, 1) The answer code of the question based on the screening method used \nQuestion 10 Answer Binary (0, 1) The answer code of the question based on the screening method used \nScreening Score Integer The final score obtained based on the scoring algorithm of the screening method used. This was computed in an automated manner.\nRelevant Papers: \n1) Tabtah, F. (2017). Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment. Proceedings of the 1st International Conference on Medical and Health Informatics 2017, pp.1-6. Taichung City, Taiwan, ACM.\n2) Thabtah, F. (2017). ASDTests. A mobile app for ASD screening. www.asdtests.com [accessed December 20th, 2017].\n3) Thabtah, F. (2017). Machine Learning in Autistic Spectrum Disorder Behavioural Research: A Review. To Appear in Informatics for Health and Social Care Journal. December, 2017 (in press)\nCitation Request:\nPlease refer to the Machine Learning Repository's citation policy", "format": "arff", "uploader": "Dustin Carrion", "uploader_id": 30123, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-23 13:13:20", "update_comment": null, "last_update": "2022-03-23 13:13:20", "licence": "CC0: Public Domain", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102239\/dataset", "kaggle_url": null, "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "Autism-Screening", "Data Set Name: Autistic Spectrum Disorder Screening Data for Adult 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 " ], "weight": 5 }, "qualities": { "NumberOfInstances": 704, "NumberOfFeatures": 21, "NumberOfClasses": null, "NumberOfMissingValues": 192, "NumberOfInstancesWithMissingValues": 95, "NumberOfNumericFeatures": 11, "NumberOfSymbolicFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 13.494318181818182, "PercentageOfMissingValues": 1.2987012987012987, "AutoCorrelation": null, "PercentageOfNumericFeatures": 52.38095238095239, "Dimensionality": 0.029829545454545456, "PercentageOfSymbolicFeatures": 0, "MajorityClassPercentage": null, "MajorityClassSize": null, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0 }, "tags": [], "features": [ { "name": "A1_Score", "index": "0", "type": "numeric", "distinct": "2", "missing": "0", "min": "0", "max": "1", "mean": "1", "stdev": "0" }, { "name": "A2_Score", "index": "1", "type": "numeric", "distinct": "2", "missing": "0", "min": "0", "max": "1", "mean": "0", "stdev": "0" }, { "name": "A3_Score", "index": "2", "type": "numeric", "distinct": "2", "missing": "0", "min": "0", "max": "1", "mean": "0", "stdev": "0" }, { "name": "A4_Score", "index": "3", "type": "numeric", "distinct": "2", "missing": "0", "min": "0", "max": "1", "mean": "0", "stdev": "1" }, { "name": "A5_Score", "index": "4", "type": "numeric", "distinct": "2", "missing": "0", "min": "0", "max": "1", "mean": "0", "stdev": "1" }, { "name": "A6_Score", "index": "5", "type": "numeric", "distinct": "2", "missing": "0", "min": "0", "max": "1", "mean": "0", "stdev": "0" }, { "name": "A7_Score", "index": "6", "type": "numeric", "distinct": "2", "missing": "0", "min": "0", "max": "1", "mean": "0", "stdev": "0" }, { "name": "A8_Score", "index": "7", "type": "numeric", "distinct": "2", "missing": "0", "min": "0", "max": "1", "mean": "1", "stdev": "0" }, { "name": "A9_Score", "index": "8", "type": "numeric", "distinct": "2", "missing": "0", "min": "0", "max": "1", "mean": "0", "stdev": "0" }, { "name": "A10_Score", "index": "9", "type": "numeric", "distinct": "2", "missing": "0", "min": "0", "max": "1", "mean": "1", "stdev": "0" }, { "name": "age", "index": "10", "type": "string", "distinct": "46", "missing": "2" }, { "name": "gender", "index": "11", "type": "string", "distinct": "2", "missing": "0" }, { "name": "ethnicity", "index": "12", "type": "string", "distinct": "11", "missing": "95" }, { "name": "jundice", "index": "13", "type": "string", "distinct": "2", "missing": "0" }, { "name": "austim", "index": "14", "type": "string", "distinct": "2", "missing": "0" }, { "name": "contry_of_res", "index": "15", "type": "string", "distinct": "67", "missing": "0" }, { "name": "used_app_before", "index": "16", "type": "string", "distinct": "2", "missing": "0" }, { "name": "result", "index": "17", "type": "numeric", "distinct": "11", "missing": "0", "min": "0", "max": "10", "mean": "5", "stdev": "3" }, { "name": "age_desc", "index": "18", "type": "string", "distinct": "1", "missing": "0" }, { "name": "relation", "index": "19", "type": "string", "distinct": "5", "missing": "95" }, { "name": "Class\/ASD", "index": "20", "type": "string", "distinct": "2", "missing": "0" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }