1. A method of constructing a classifier for disease detection, adapted to an electronic apparatus, comprising: constructing a codebook of representative features based on a plurality of disease-irrelevant data, wherein the disease-irrelevant data is non-medical data; extracting a plurality of transfer-learned disease features from a plurality of disease-relevant bio-signals according to the codebook, wherein both the disease-irrelevant data and the disease-relevant bio-signals are time-series data, and the disease-irrelevant data comprises a plurality of audio signals; and performing supervised learning based on the transfer-learned disease features to train the classifier for disease detection, wherein the steps of extracting a plurality of transfer-learned disease features from the plurality of disease-relevant bio-signals according to the codebook and performing supervised learning based on the transfer-learned disease features to train the classifier for disease detection comprise: extracting a plurality of first transfer-learned disease features from a plurality of heart sound signals according to the codebook; extracting a plurality of second transfer-learned disease features from a plurality of electrocardiac (ECG) signals according to the codebook; concatenating the first transfer-learned disease features and the second transfer-learned disease features to form a plurality of fused feature vectors; and performing supervised learning on the fused feature vectors to train the classifier for disease detection.