Integrating traditional Chinese pulse diagnosis with machine learning: novel approaches for pregnancy and coronary heart disease identification

This study integrated ancient Traditional Chinese Medicine (TCM) pulse diagnosis techniques with modern machine learning to advance contemporary medical diagnostics. A portable intelligent TCM pulse diagnostic device was developed using MEMS and CMOS technologies to collect detailed pulse waveform data from three groups: healthy females, females with coronary heart disease (CHD), and pregnant females in the first trimester. Three feature selection methods-Random Forest, Recursive Feature Elimination, and correlation analysis were adopted to identify key pulse features. Thirteen classification algorithms using various combinations of feature selection and machine learning approaches were evaluated. This study achieved a maximum classification accuracy of 80% using solely pulse data. Incorporating clinical parameters such as temperature and age significantly increased classification accuracy to 91%. The Gradient Boosting Decision Tree model with correlation analysis-based feature selection and Random Forest-based dimensionality reduction demonstrated the highest accuracy. Key pulse features identified included those related to the Liver Meridian, Pericardium Meridian, and Heart Meridian, which are crucial for differentiating the three groups. This study validated the clinical utility of pulse diagnostic devices when combined with machine learning algorithms. This study demonstrates concrete clinical utility for the pulse diagnostic device, while empirically validating foundational TCM concepts. It establishes a rigorous framework to advance evidence-driven modernization of pulse diagnosis protocols.