Digital health · 2024
Toward an intelligent computing system for the early diagnosis of Alzheimer's disease based on the modular hybrid growing neural gas.
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Cabrera-León Ylermi, Fernández-López Pablo, García Báez Patricio, Kluwak Konrad, Navarro-Mesa Juan Luis, Suárez-Araujo Carmen Paz, Alzheimer’s Disease Neuroimaging Initiative *
Аннотация
OBJECTIVE: The proportion of older people will soon include nearly a quarter of the world population. This leads to an increased prevalence of non-communicable diseases such as Alzheimer's disease (AD), a progressive neurodegenerative disorder and the most common dementia. mild cognitive impairment (MCI) can be considered its prodromal stage. The early diagnosis of AD is a huge issue. We face it by solving these classification tasks: MCI-AD and cognitively normal (CN)-MCI-AD.
METHODS: An intelligent computing system has been developed and implemented to face both challenges. A non-neural preprocessing module was followed by a processing one based on a hybrid and ontogenetic neural architecture, the modular hybrid growing neural gas (MyGNG). The MyGNG is hierarchically organized, with a growing neural gas (GNG) for clustering followed by a perceptron for labeling. For each task, 495 and 819 patients from the Alzheimer's disease neuroimaging initiative (ADNI) database were used, respectively, each with 211 characteristics.
RESULTS: Encouraging results have been obtained in the MCI-AD classification task, reaching values of area under the curve (AUC) of 0.96 and sensitivity of 0.91, whereas 0.86 and 0.9 in CN-MCI-AD. Furthermore, a comparative study with popular machine learning (ML) models was also performed for each of these tasks.
CONCLUSIONS: The MyGNG proved to be a better computational solution than the other ML methods analyzed. Also, it had a similar performance to other deep learning schemes with neuroimaging. Our findings suggest that our proposal may be an interesting computing solution for the early diagnosis of AD.
METHODS: An intelligent computing system has been developed and implemented to face both challenges. A non-neural preprocessing module was followed by a processing one based on a hybrid and ontogenetic neural architecture, the modular hybrid growing neural gas (MyGNG). The MyGNG is hierarchically organized, with a growing neural gas (GNG) for clustering followed by a perceptron for labeling. For each task, 495 and 819 patients from the Alzheimer's disease neuroimaging initiative (ADNI) database were used, respectively, each with 211 characteristics.
RESULTS: Encouraging results have been obtained in the MCI-AD classification task, reaching values of area under the curve (AUC) of 0.96 and sensitivity of 0.91, whereas 0.86 and 0.9 in CN-MCI-AD. Furthermore, a comparative study with popular machine learning (ML) models was also performed for each of these tasks.
CONCLUSIONS: The MyGNG proved to be a better computational solution than the other ML methods analyzed. Also, it had a similar performance to other deep learning schemes with neuroimaging. Our findings suggest that our proposal may be an interesting computing solution for the early diagnosis of AD.