Feature Selection and Machine Learning Applied for Alzheimer’s Disease Classification

Sanchez-Reyna, A. G.; Espino-Salinas, Carlos H.; Rodríguez-Aguayo, Pablo C.; Salinas-González, Jared D.; Zanella-Calzada, Laura A.; Martínez-Escobar, Elda Y.; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.

doi:10.1007/978-3-030-30648-9_17

Cited by 1 publication

(1 citation statement)

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“…The XG-Boost classifier on the ADNI dataset achieved an accuracy of 85.92% [10]. When using CAD-based approaches to detect AD, the genetic package GALGO for feature selection and classifiers such as RF, logistic regression (LR), SVM, and artificial neural networks (ANN) achieved better results compared to state-of-the-art methods with an AUC of 0.842 [11]. A RF-based feature selection model featuring a Gaussian-inspired algorithm achieved a higher classification accuracy of 78.8% over the SVM classifier and a classification accuracy of 75.6% for determining between patients with CN vs. EMCI [12].…”

Section: Introductionmentioning

confidence: 99%

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Alharbi¹,

Ziyad²

2023

Computers, Materials &Amp; Continua

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Aging is a natural process that leads to debility, disease, and dependency. Alzheimer's disease (AD) causes degeneration of the brain cells leading to cognitive decline and memory loss, as well as dependence on others to fulfill basic daily needs. AD is the major cause of dementia. Computer-aided diagnosis (CADx) tools aid medical practitioners in accurately identifying diseases such as AD in patients. This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop (IWD) algorithm and the Random Forest (RF) classifier. The IWD algorithm an efficient feature selection method, was used to identify the most deterministic features of AD in the dataset. RF is an ensemble method that leverages multiple weak learners to classify a patient's disease as either demented (DN) or cognitively normal (CN). The proposed tool also classifies patients as mild cognitive impairment (MCI) or CN. The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The RF ensemble method achieves 100% accuracy in identifying DN patients from CN patients. The classification accuracy for classifying patients as MCI or CN is 92%. This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

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Section: Introductionmentioning

confidence: 99%