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A Hybrid Intelligent Classification System for Geriatric Frailty Syndrome Prevention and Control

Published: 28 June 2024 Publication History

Abstract

As a typical condition of geriatric syndromes, it is speculated that frailty syndrome arises from dysfunctional feedback mechanisms among interacting physiological systems. To assess the frailty of the elderly for effective prevention and control in comprehensive geriatric syndromes medical base, this paper proposes a hybrid intelligent approach. Firstly, the approach utilizes the grip strength change rate as an indicator, employs Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for clustering elderly individuals and refines the clustering results. Subsequently, Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTE-ENN) is applied for oversampling and undersampling of samples, enhancing model performance by combining synthetic minority class samples (SMOTE) and removing redundancy in majority class samples (ENN). Finally, the Slime Mould Algorithm (SMA) is employed to optimize the hyperparameters of Categorical Boosting (CatBoost), enhancing classification accuracy. The method is trained using medical examination results and validated for classification accuracy on a test dataset. Compared to CatBoost, SMOTE-ENN-SMA-CatBoost exhibits improvements of 7.04%, 7.76%, and 7.52% in accuracy, precision, and F1 score, respectively. This integrated framework provides an effective solution for assessing frailty probability in the elderly population, enhancing accuracy and reliability.

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  1. A Hybrid Intelligent Classification System for Geriatric Frailty Syndrome Prevention and Control

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    BIC '24: Proceedings of the 2024 4th International Conference on Bioinformatics and Intelligent Computing
    January 2024
    504 pages
    ISBN:9798400716645
    DOI:10.1145/3665689
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 28 June 2024

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