Asthma Detection Research Based on Voice Signal Processing and Machine Learning.

To analyze voice signals and identify asthma patients using voice signal analysis and machine learning techniques, we collected clear, low-noise fixed-pattern voice signals from 50 asthma patients and 50 healthy controls to build an analysis database. The research conducted multi-dimensional voice signal analysis based on MATLAB and selected voice feature indicators with significant differences between asthma patients and healthy controls. After dimensionality reduction analysis on differential phonetic features, the processed features were incorporated into subsequent SVM and RF modeling and classification research.

Research on Diagnosis Prediction of Traditional Chinese Medicine Diseases Based on Improved Bayesian Combination Model.

Traditional Chinese Medicine (TCM) clinical intelligent decision-making assistance has been a research hotspot in recent years. However, the recommendations of TCM disease diagnosis based on the current symptoms are difficult to achieve a good accuracy rate because of the ambiguity of the names of TCM diseases. The medical record data downloaded from ancient and modern medical records cloud platform developed by the Institute of Medical Information on TCM of the Chinese Academy of Chinese Medical Sciences (CACMC) and the practice guidelines data in the TCM clinical decision supporting system were utilized as the corpus.