Effective detection of Alzheimer's disease by optimizing fuzzy K-nearest neighbors based on salp swarm algorithm

• Published in: Computers in biology and medicine Vol. 159; p. 106930
• Main Authors: Lu, Dongwan, Yue, Yinggao, Hu, Zhongyi, Xu, Minghai, Tong, Yinsheng, Ma, Hanjie
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2023; Elsevier Limited
• Subjects: Accuracy; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Artificial Intelligence; Brain; Classification; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Datasets; Diagnosis; Disease control; Feature selection; Fuzzy sets; Humans; Machine learning; Magnetic Resonance Imaging - methods; Medical diagnosis; Neural networks; Neurodegenerative diseases; Optimization; Performance enhancement; Salp swarm algorithm; Support vector machines; Swarm intelligence algorithm; Salp swarm algorithm; Swarm intelligence algorithm; Feature selection; Medical diagnosis; Alzheimer's disease
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.106930

Alzheimer's disease (AD) is a typical senile degenerative disease that has received increasing attention worldwide. Many artificial intelligence methods have been used in the diagnosis of AD. In this paper, a fuzzy k-nearest neighbor method based on the improved binary salp swarm algorithm (IBSSA-FKNN) is proposed for the early diagnosis of AD, so as to distinguish between patients with mild cognitive impairment (MCI), Alzheimer's disease (AD) and normal controls (NC). First, the performance and feature selection accuracy of the method are validated on 5 different benchmark datasets. Secondly, the paper uses the Structural Magnetic Resolution Imaging (sMRI) dataset, in terms of classification accuracy, sensitivity, specificity, etc., the effectiveness of the method on the AD dataset is verified. The simulation results show that the classification accuracy of this method for AD and MCI, AD and NC, MCI and NC are 95.37%, 100%, and 93.95%, respectively. These accuracies are better than the other five comparison methods. The method proposed in this paper can learn better feature subsets from serial multimodal features, so as to improve the performance of early AD diagnosis. It has a good application prospect and will bring great convenience for clinicians to make better decisions in clinical diagnosis. •A fuzzy k-nearest neighbor method based on the improved binary salps swarm algorithm (IBSSA-FKNN) is proposed.•The proposed method is compared with some algorithms on UCI dataset and AD dataset respectively.•The results reveal the outstanding performance of the proposed algorithm.