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Investigating the Impact of Various Feature Selection Techniques on the Attributes Used in the Diagnosis of Alzheimer’s Disease

  • S. R. Bhagyashree
  • Muralikrishna
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

According to the Dementia India report 2010, it is estimated that over 3.7 million people are affected by dementia and is expected to be double by 2030. Around 60–80% of the demented are suffering from Alzheimer’s disease. Neuropsychological tests are useful tools for diagnosis of dementia. Diagnosis of dementia using machine learning for low- and middle-income setting is a rare study. Various attributes are used for diagnosing dementia. Finding the prominent attributes among them is a tedious job. Chi-squared, gain ratio, info gain and ReliefF filtering techniques are used for finding the prominent attributes. Cognitive score is identified as the most prominent attribute.

Keywords

Alzheimer’s disease Chi-square Gain ratio Info gain ReliefF Naïve Bayes 

Notes

Acknowledgements

We are grateful to the participants and their family members for taking part in this study. Our sincere thanks to Principal and Management of ATME College of Engineering.

Ethics

The ethics approval for this study was obtained from the Ethics Committee of CSI Holdsworth Memorial Hospital, Mysore.

Only those participants who were able to provide fully informed consent participated in this study and the informed consent was obtained from the participants.

Conflict of Interest

None of the authors have any conflict of interest to declare.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.E & C DepartmentATME College of EngineeringMysoreIndia
  2. 2.Wellcome DBT AllianzCSI Holdsworth Memorial HospitalMysoreIndia

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