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Conference articles · 2022

MPredA: A Machine Learning Based Prediction System to Evaluate the Autism Level Improvement

Masud Rabbani, Munirul M. Haque, Dipranjan Das Dipal, Md Ishrak Islam Zarif, Anik Iqbal, Amy Schwichtenberg, Naveen Bansal, Tanjir Rashid Soron, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed

Pervasive Computing Technologies for Healthcare (PH 2021) · Volume 431 · Pages 416–432

Abstract

This paper describes the developmental process of a machine learning-based prediction system to evaluate autism Improvement level (MPredA), where the concerned user (parents or clinical professionals) can evaluate their children’s development through the web application. We have deployed our previous work (mCARE) data from Bangladesh for prediction models. This system can predict four major milestone parameter improvement levels of children with ASD. In this four-broad category, we have classified into four sub-milestones parameters for each of them to predict the detailed improvement level for each child with ASD. This MPredA can predict 16 milestone parameters for every child with ASD. We deployed four machine learning algorithms (Decision Tree, Logistic Regression, K-Nearest Neighbor, and Artificial Neural Network) for each parameter with 1876 data of the children with ASD to develop 64 prediction models. Among the 64 models, we selected the most accurate 16 models (based on the model’s accuracy and evaluation scores) to convert pickles file for the MPredA web-based application. For the prediction system, we have determined the most ten important demographic information of the children with ASD. Among the four-machine learning algorithms, the decision tree showed the most significant result to build the MPredA web-based application. We also test our MPredA -web application by white box testing and get 97.5% of accuracy with real data.

Publication details

Category
Conference articles
Authors & affiliations
  1. Masud RabbaniUbicomp Lab, Department of Computer Science, Marquette University, Milwaukee, WI, USA
  2. Munirul M. HaqueR. B. Annis School of Engineering, University of Indianapolis, Indianapolis, IN, USA
  3. Dipranjan Das DipalUbicomp Lab, Department of Computer Science, Marquette University, Milwaukee, WI, USA
  4. Md Ishrak Islam ZarifUbicomp Lab, Department of Computer Science, Marquette University, Milwaukee, WI, USA
  5. Anik IqbalUbicomp Lab, Department of Computer Science, Marquette University, Milwaukee, WI, USA
  6. Amy SchwichtenbergCollege of Health and Human Sciences, Purdue University, West Lafayette, IN, USA
  7. Naveen BansalDepartment of Mathematical and Statistical Sciences, Marquette University, Milwaukee, WI, USA
  8. Tanjir Rashid SoronTelepsychiatry Research and Innovation Network Ltd, Dhaka, Bangladesh
  9. Syed Ishtiaque AhmedDepartment of Computer Science, University of Toronto, Toronto, ON, Canada
  10. Sheikh Iqbal AhamedUbicomp Lab, Department of Computer Science, Marquette University, Milwaukee, WI, USA
Venue
Pervasive Computing Technologies for Healthcare (PH 2021)
Publisher
Springer, Cham
Record
Volume 431 · Pages 416–432
Published
March 23, 2022
Cite this work

Masud Rabbani, Munirul M. Haque, Dipranjan Das Dipal, Md Ishrak Islam Zarif, Anik Iqbal, Amy Schwichtenberg, Naveen Bansal, Tanjir Rashid Soron, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed. “MPredA: A Machine Learning Based Prediction System to Evaluate the Autism Level Improvement”. Pervasive Computing Technologies for Healthcare (PH 2021), 431, 416–432. (2022). https://doi.org/10.1007/978-3-030-99194-4_26.

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Research topics

  • Autism Spectrum Disorder (ASD)
  • Milestone Parameter (MP)
  • Prediction of MP Improvement
  • Demography of children with ASD
  • Importance of demography
  • autism spectrum disorder
  • milestone prediction
  • machine learning
  • mCARE
  • clinical decision support
  • web application

Research context

Explore the connected research focus

Mobile-health AI Autism care & mCARE