Classification and prediction of Alzheimer's disease based on machine learning KAN network

Wenxin Xu; Sihan Long; Jiayu Shi

doi:10.54097/jk425925

Authors

Wenxin Xu
Sihan Long
Jiayu Shi

DOI:

https://doi.org/10.54097/jk425925

Keywords:

Alzheimer’s disease; Random forest; Confusion matrix; Bayesian optimization.

Abstract

Alzheimer's disease can cause memory and cognitive decline, but early and accurate diagnosis is not easy. With the continuous enrichment of neuroimaging data, the progress of genetics, biochemistry, and other disciplines has made it possible to study Alzheimer’s disease (AD) in multiple dimensions. The ever-evolving machine learning and deep learning algorithms overcome the shortcomings of high dimensionality and small sample size of neuroimaging, and provide advanced models for clinical intelligent non-invasive diagnosis. This article reviews the application of machine learning methods and strategies based on the ADNI database in the early diagnosis of mild cognitive dysfunction and AD, and uses random forest, support vector machine, multilayer perceptron (MLP), and Kolmogorov-Arnold Networks (KAN) to predict and classify the early clinical diagnosis of Alzheimer's disease. The high accuracy of KAN network indicates that the machine learning method is helpful to improve the accuracy of predicting AD subtypes.

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