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Data Mining to Support the Discrimination of Amyotrophic Lateral Sclerosis Diseases Based on Gait Analysis

  • Haya Alaskar
  • Abir Jaafar Hussain
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

In many medical researches, dealing with huge dataset is crucial. However, it is difficult to use standard methodologies to analysis huge dataset. In such cases, data mining tools can support doctors for better diagnosis. Data mining tools have been utilized in clinical data analysis such as biological signals, clinical images to analysis and detect diseases. The utilization of these techniques can increase diagnostic sensitivity and specificity. It can help reduce the misdiagnosis, in addition to early prediction. This paper discusses the use of machine learning algorithms for the detection and classification of amyotrophic lateral sclerosis disease using gait data. Our analysis indicated that a number of machine learning algorithms such as the linear discriminant classifier and quadratic discriminant classifier can discriminate between normal and abnormal cases of amyotrophic lateral sclerosis disease.

Keywords

Machine learning Neuro-degenerative disease Amyotrophic Lateral Sclerosis 

Notes

Acknowledgment

This project was funded by Prince Sattam University with grant number 2017/01/7814.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science DepartmentPrince Sattam UniversityAlkharjSaudi Arabia
  2. 2.Department of Computer ScienceLiverpool John Moores UniversityLiverpoolUK

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