KNN-based classification on Alzheimer's disease data after dimensionality reduction using principal component analysis

Artificial intelligence techniques have been deeply involved in the heterogeneous data aspects of biomedical applications. However, the high dimensionality and computational complexity of data can make classification, pattern recognition and data visualization difficult. Choosing appropriate dimensionality reduction techniques can help increase processing speed, reduce the time and effort required to extract valuable information, and ensure high accuracy. In this study, Alzheimer's disease data were taken as an example. Individual cases with missing values were removed, and non-digital data were converted to digital data using Min-Max normalization. Then principal component analysis (PCA) was applied to map the original feature space to 1 dimension and the variance of the validation set was calculated by 5-fold cross-validation to find the appropriate K value. The results showed that when PCA was applied to reduce the data to 1 dimension, the AUC (95% confidence interval) of the KNN classifier reached 0.898 ± 0.014, which was 30.4%higher than the case without PCA. Our current findings suggest that in many busy clinics and hospitals, it is quite worthwhile to use dimensionality reduction methods to save model computing time and to use KNN models to obtain better accuracy.