Detection of Alzheimer's disease by Raman spectra of rat's platelet with a simple feature selection

A novel method using feature selection is proposed to classify Alzheimer's disease using Raman spectra. The method first find all the significant peak from the preprocessed spectrum as the feature candidates for classification. We select the most discrimination peak as a reference feature and compute the correlation coefficients between the reference and every peaks chosen. Then we discard highly correlated features to reduce the number of possible feature candidates. With the peak value and their ratio of the remaining features, we carry out the preliminary classification experiments and examine top 10% cases to seek the most frequently appearing features. Among them, we choose top 2 features, intensity of 1658 cm− 1 and ratio of intensity of 757 and 743 cm− 1. These features correspond to protein bands of Amide I mode and cytochrome c, which are also considered important for the detection of Alzheimer's disease by other researchers. The classification result using 278 spectra achieved 95.8% classification rates for MLP (multi-layer perceptron) with these two features. It confirms that the features chosen with the proposed method could be effectively used for the diagnosis of Alzheimer's disease.