Enhancing MCI Detection with a Hybrid Machine Learning Approach

DOI

10.2991/978-94-6463-314-6_14How to use a DOI?

Keywords

Hybrid ML; Alzheimer’s Disease; MCI; RNN; CNN; RF; SVM

Abstract

Mild Cognitive Impairment (MCI) is a prodromal stage of dementia, is often native with very high risk of evolving into Alzheimer’s disease. Early detection and accurate classification of MCI can significantly aid in timely intervention and personalized treatment planning. In the study conducted, we put forward a hybrid machine learning approach to enhancing MCI detection using a combination of feature engineering, feature selection, and ensemble learning algorithm by using standalone Recurrent Neural Networks (RNN) as well as Convolutional Neural Networks (CNN). The proposed method leverages the temporal dependencies captured by RNN and the spatial information extracted by CNN to increase the robustness and accuracy of MCI classification. We used a comprehensive dataset ADNI consisting of neuroimaging and clinical data from a large cohort of subjects, including MCI sufferers and healthy controls. The neuroimaging data encompassed structural MRI scans, while the clinical data encompassed various cognitive assessments. Neuroimaging data is preprocessed to extract relevant features and combine them with the clinical data to create a unified input representation for the hybrid model.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - B. A. Sujathakumari
AU  - Sudharshan Patil Kulkarni
AU  - A. Chinmaya
AU  - H. J. Suhas
AU  - K. G. Jayanth
AU  - R. Gowtham
PY  - 2023
DA  - 2023/12/21
TI  - Enhancing MCI Detection with a Hybrid Machine Learning Approach
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 142
EP  - 151
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_14
DO  - 10.2991/978-94-6463-314-6_14
ID  - Sujathakumari2023
ER  -