Abstract
Alzheimer’s Disease (AD) is a neurodegenerative disorder primarily characterized by deteriorating cognitive functions. In 2016 an estimated 40 million people were diagnosed with AD, and the expectation for 2050 is 131 million. Therefore, healthcare systems require detecting and confirming AD at its different stages to provide adequate and accurate treatments. Recently, Machine Learning (ML) models have been used to classify AD’s stages. It has become a priority to develop a framework for AD’s stages detection based on ML and imputation methods capable of handling datasets with missing values while providing high accuracy. We propose a ML computational framework that integrates data processing, feature selection, imputation methods and 5 different ML models. The performance of the proposed framework has been evaluated using the main metrics for classification problem; accuracy, F1- score, recall, and precision. As a results of the proposed process, our framework classifies the AD’s onsets with an accuracy of 99%.
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Acknowledgment
The authors would like to acknowledge NIH BioMed Grant Number 150108136 under Florida A &M University, and CI-New: Cognitive Hardware and Software Ecosystem Community Infrastructure for allow us to run our application in their infrastructure (Nautilus).
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Theran-Suarez, C., Bautista, Y.J.P., Adankai, V., Aló, R. (2023). Machine Learning Computational Framework for Alzheimer’s Disease Stages Classification. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 3. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 561. Springer, Cham. https://doi.org/10.1007/978-3-031-18344-7_26
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DOI: https://doi.org/10.1007/978-3-031-18344-7_26
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