An evolutionary ensemble modeling (EEM) method is developed to improve the accuracy of warfarin dose prediction. In EEM, genetic programming (GP) evolves diverse base models, and the genetic algorithm optimizes the parameters of the GP. The EEM model is assembled by using the prepared base models through a technique called “bagging.” In the experiment, a dataset of 289 Chinese patients, which was provided by the First Affiliated Hospital of Soochow University, is used for training, validation, and testing. The EEM model with selected feature groups is benchmarked with four machine-learning methods and three conventional regression models. Results show that the EEM model with the M2+G group, namely age, height, weight, gender, CYP2C9, VKORC1, and amiodarone, presents the largest coefficients of determination (R ² ), the highest percentage of the predicted dose within 20% of the actual dose (20%-p), the smallest mean absolute error, mean squared error, and root-mean-squared error on the test set, and the least decrease in R ² from the training set to the test set. In conclusion, the EEM method with M2+G delivers superior performance and can, therefore, be a suitable prediction model of warfarin dose for clinical applications.

Aroma and taste have long been considered important indicators of quality coffee. Specialty coffee, that is, coffee from a single estate, farm, or village in a coffee-growing region, in particular, has a unique aroma that reflects the coffee-producing region. In order to enable the traceability of coffee origin, in this study we have developed an e-nose system to discriminate the aroma of freshly roasted coffee in different production regions. In the case study, we employed the e-nose system to experiment with various machine learning models for recognizing several collected coffee beans such as coffees from Yirgacheffe and Kona. Additionally, our contribution also includes the development of a method to create an aromatic digital fingerprint of a specific coffee bean to identify its origin. The experimental results show that the developed e-nose system achieves good recognition performance for coffee aroma recognition. The extracted digital fingerprints have great potential to be stored in an extensible coffee aroma database similar to a comprehensive library of specific coffee bean aroma characteristics, for traceability and reconfirmation of their origin.

The prediction of daily stable warfarin dosage for a specific patient is difficult. To improve the predictive accuracy and build a highly accurate predictive model, we developed an ensemble learning method, called evolutionary fuzzy c-mean (EFCM) clustering algorithm with support vector regression (SVR). A dataset of 517 Han Chinese patients was collected from the data of The First Affiliated Hospital of Soochow University and dataset of International Warfarin Pharmacogenetics Consortium for training and testing. In EFCM+SVR, we adopted SVR to build a generalized base model (SVR model). To achieve an accurate prediction on patients with large dosage, we proposed an EFCM clustering algorithm that can be used to cluster the training set and designed a clustering model on clusters and centroids. The SVR and clustering models were integrated into an ensemble model by stepwise functions. In the experiment, three artificial neural networks, SVR, two ensemble models and three regression models were used as comparators to the EFCM+SVR model, which obtained the smallest mean absolute error (0.67 mg/day) in warfarin dose prediction and the largest R-squared (43.9%). The model achieved satisfactory prediction in terms of the percentage of patients whose predicted dose of warfarin was within 15% and 20% of the actual stable therapeutic dose (15%-p of 36% and 20%-p of 46.6%).