Computer-Aided-Prediction of Body Constitution with Efficient Cock-Tail Learning

In this paper, the clinical data thru the questionnaire of body constitution (BC) is analyzed by multiple efficient machine learning algorithms for wide use in traditional Chinese medicine (TCM). This research aims at precisely categorizing the BCs from the life-style; offering the health guidance on the life-styles for recovering the so-called "biased" BCs to the healthy status known as the "Gentle BC". The key features of life-style are identified by machine learning (ML). However, the conventional sole ML algorithm for such application, known as random forest (RF), partial least squares (PLS), or least absolute shrinkage and selection operator (LASSO) hardly offers a small set of significant life-style features. In this work, a special scheme of LASSO learning technology is developed for identifying the reasonably few medical features and improve the diagnosis accuracy simultaneously. By pairing each "biased" BC against the gentle BC, the categorization task is conducted with reduced features. Similarly to the federated learning process, the common features among multiple algorithms are refined. From the real clinical data validation, the BC categorization accuracy is 94.6% which is 24.7% higher than the state-of-the-art (SOTA) works; the average key features are reduced to 17 where the best effort of SOTA is 31. Finally, the common key features are summarized among multiple algorithms.