Disease Detection Using Wrist Pulse Analysis

Early detection of diseases is crucial for effective treatment and management. Traditionally, disease detection involves invasive and costly medical procedures. However, recent advancements in non-invasive methods have proven highly successful in identifying various illnesses. The wrist pulse has long been an important tool for detecting diseases, with traditional Chinese medicine making extensive use of this method. It shows great promise in diagnosing a wide range of conditions. This study provides a detailed analysis of research on wrist pulse analysis and its applications in disease detection. It examines the physiological basis of wrist pulse analysis, focusing on the relationship between underlying medical conditions and the characteristics of wrist pulses. Additionally, the study explores how wearable pulse detectors and machine learning algorithms can improve the accuracy and effectiveness of wrist pulse analysis. In this research, we use a dataset of 300 samples from various diseases, analyzing it with MATLAB and applying ensemble learning algorithms. We have achieved accuracies above 80% for nearly all algorithms, and accuracy can be further improved by expanding the dataset with more samples and extracting additional features.