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.
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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.
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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.
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Bhagyashree, S.R., Muralikrishna (2019). Investigating the Impact of Various Feature Selection Techniques on the Attributes Used in the Diagnosis of Alzheimer’s Disease. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_166
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