Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm

Abstract

In this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The methodology is structured in two phases. In a first phase, the most relevant volumes of interest (VOI) of the brain are selected by means of an evolutionary multi-objective optimization (MOE) algorithm. Each of these VOIs are subjected to volumetric feature extraction using the Three-Dimensional Discrete Wavelet Transform (3D-DWT). When applying 3D-DWT, a high number of coefficients is obtained, requiring the use of feature selection/reduction algorithms to find the most relevant features. The method used in this contribution is based on Mutual Redundancy (MI) and Minimum Maximum Relevance (mRMR) and PCA. To optimize the VOI selection, a first group of 550 MRI was used for the 5 classes: PD, SWEDD, Prodromal, GeneCohort and Normal. Once the Pareto Front of the solutions is obtained (with varying degrees of complexity, reflected in the number of selected VOIs), these solutions are tested in a second phase. In order to analyze the SVM classifier accuracy, a test set of 367 MRI was used. The methodology obtains relevant results in multi-class classification, presenting several solutions with different levels of complexity and precision (Pareto Front solutions), reaching a result of 97% as the highest precision in the test data.

Fine hippocampal morphology analysis with a multi-dataset cross-sectional study on 2911 subjects

“…Alzheimer's disease (AD) progress could be classified into the following stages (groups) ( Bi et al, 2018 ), namely pre-clinical/cognitively normal (CN), early mild cognitive impairment (EMCI) ( Edmonds et al, 2019 ), late MCI (LMCI), and AD stage. In contrast, Parkinson's disease (PD) is typically categorized into three developmental stages ( Rojas-Valenzuela et al, 2022 ), including cognitively normal, prodromal phase of PD ( Roos et al, 2022 ), and PD. While previous studies ( Junqué et al, 2005 , Leung et al, 2010 ) have reported reductions in hippocampal volume among patients with AD and PD, there has been limited attention given to other morphological changes.…”

Multiobjective Optimization of Fuzzy System for Cardiovascular Risk Classification

“…This approach seeks to optimize multiple objectives concurrently by considering the interdependencies between them and the associated trade-offs. The Pareto front is a prevalent mechanism in the field of healthcare, particularly in the context of cardiovascular disease, which serves as a means of visualizing the trade-offs involved in decision-making and pinpointing the most desirable solutions [9][10][11]. As an illustration, a metaheuristic multiobjective optimization approach is utilized in a study to identify Alzheimer's disease through the use of multimodal data [9].…”