Apply Data Analytics Techniques on Autism dataset to discover hidden patterns that would be leveraged in decision making.

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-Child) plus ten individuals characteristics that have proved to be effective in detecting the ASD cases from controls in behaviour science.

Data Set Name: Autistic Spectrum Disorder Screening Data for Children

###Source:

Fadi Fayez Thabtah

Department of Digital Technology Manukau Institute of Technology, Auckland, New Zealand

[email protected]

Project timeline https://docs.google.com/spreadsheets/d/1Tmqhz15BhxVzc2wUCyTLsCGPpnZC9Vtyme5eR9ixX7Q/edit?usp=sharing

Descriptive Statistics

A square representation was equated to blue as positive and red as negative. A larger dot represents a higher degree of correlation and the matrix shows that the squares are symmetrically diagonal meaning the variables are positively correlated with itself. Also, some variables displays positive correlation with each other.

Exporatory Data Analysis using funModeling Package an approach to listen to the data.

Experiments carried out in WEKA Tool to select a model with good accuracy that would produce the least rules that would be then employed as a mapper to a description reoprt.