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.

Навыки: Статистический анализ, Статистика, Анализ и обработка данных, Machine Learning (ML), Обработка данных

О клиенте:
( 1 отзыв ) Toronto, Canada

ID проекта: #33664515

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