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A Web-Based Model to Predict a Neurological Disorder Using ANN.

Abdulwahab Ali Almazroi1, Hitham Alamin1, Radhakrishnan Sujatha2

  • 1Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah 21959, Saudi Arabia.

Healthcare (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces an artificial neural network (ANN) to predict dementia

Keywords:
brain disorderdata imputationdementiaperformance measuresscaled conjugate gradient

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Data Science

Background:

  • Dementia is a progressive cognitive decline, impacting memory, logic, and social interaction, leading to disability.
  • Misunderstanding dementia often delays crucial early treatment and support.
  • It is a major cause of dependency and disability in older adults globally.

Purpose of the Study:

  • To develop and implement an artificial neural network (ANN) for predicting dementia's impact.
  • To utilize a data imputation process for enhancing prediction accuracy.
  • To create a web-based interface for accessible dementia impact prediction.

Main Methods:

  • An artificial neural network (ANN) model was developed for dementia prediction.
  • The scaled conjugate gradient (SCG) algorithm was used for model training.
  • Data imputation techniques were applied to the dataset.

Main Results:

  • The ANN achieved high accuracy: 95% (training), 85.7% (validation), and 89.3% (test).
  • Minimal cross-entropy error rates indicate robust model performance.
  • Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) were generated for all phases.

Conclusions:

  • The developed ANN effectively predicts the impact of dementia with high accuracy.
  • The study demonstrates the utility of ANNs and data imputation in dementia research.
  • A functional web-based interface facilitates practical application of the predictive model.