Building a Machine Learning Model to predict whether a patient has Cancer disease or not based on provided data

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 19 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.