Dibakar Barua

Work place: Department of Computer Science, American International University-Bangladesh (AIUB)

E-mail: dibakar254@gmail.com


Research Interests: Computational Science and Engineering, Computer systems and computational processes, Artificial Intelligence, Computational Learning Theory, Computer Architecture and Organization, Data Structures and Algorithms


Dibakar Barua earned his bachelor's degree in Computer Science and Software Engineering at Department of Computer Science, American International University-Department Bangladesh. His research focuses on Artificial Intelligence, Machine Learning and Data Science.

Author Articles
Development of an Interactive Dashboard for Analyzing Autism Spectrum Disorder (ASD) Data using Machine Learning

By Avishek Saha Dibakar Barua Mahbub C. Mishu Ziad Mohib Sumaya Binte Zilani Choya

DOI: https://doi.org/10.5815/ijitcs.2022.04.02, Pub. Date: 8 Aug. 2022

Autism Spectrum Disorder (ASD) is a neuro developmental disorder that affects a person's ability to communicate and interact with others for rest of the life. It affects a person's comprehension and social interactions. Furthermore, people with ASD experience a wide range of symptoms, including difficulties while interacting with others, repeated behaviors, and an inability to function successfully in other areas of everyday life. Autism can be diagnosed at any age and is referred to as a "behavioral disorder" since symptoms usually appear in the life's first two years. The majority of individuals are unfamiliar with the illness and so don't know whether or not a person is disordered. Rather than aiding the sufferer, this typically leads to his or her isolation from society. The problem with ASD starts in childhood and extends into adolescence and adulthood. In this paper, we studied 25 research articles on autism spectrum disorder (ASD) prediction using machine learning techniques. The data and findings of those publications using various approaches and algorithms are analyzed. Techniques are primarily assessed using four publicly accessible non-clinically ASD datasets. We found that support vector machine (SVM) and Convolutional Neural Network (CNN) provides most accurate results compare to other techniques. Therefore, we developed an interactive dashboard using Tableau and Python to analyze Autism data.

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