Towards transparent machine learning models using feature sensitivity algorithm

Despite advances in health care, diabetic ketoacidosis (DKA) remains a potentially serious risk for diabetes. Directing diabetes patients to the appropriate unit of care is very critical for both lives and healthcare resources. Missing data occurs in almost all machine learning models, especially in production. Missing data can reduce the predictive power and produce biased estimates of models. Estimating a missing value around a 50 percent probability may lead to a completely different decision. The objective of this paper was to introduce a feature sensitivity score using the proposed feature sensitivity algorithm. The data were electronic health records contained 644 records and 28 attributes. We designed a model using a random forest classifier that predicts the likelihood of a developing patient DKA at the time of admission. The model achieved an accuracy of 80 percent using five attributes; this new model has fewer features than any model mentioned in the literature review. Also, Feature sensitivity score (FSS) was introduced, which identifies within feature sensitivity; the proposed algorithm enables physicians to make transparent, and accurate decisions at the time of admission. This method can be applied to different diseases and datasets

Ensemble learning for wind profile prediction with missing values

In this paper, we aim to develop computational intelligence approaches for wind profile prediction. Specifically, we focus on two aspects in this work. First, we investigate the missing value recovery for wind data. Due to the complexity of data collection in such processes, wind data normally include missing values. Therefore, how to effectively recover such missing values for learning and prediction is an important aspect for wind profile prediction. Second, we develop an ensemble learning approach based on multiple neural network models. Our proposed method uses a new strategy based on the temporal information to assign the weights for each model dedicated for wind profile prediction to achieve better prediction performance. Various simulation studies and statistical testing demonstrate the effectiveness of our approach. © 2011 Springer-Verlag London Limited