Multivariate binary classification of imbalanced datasets-A case study based on high-dimensional multiplex autoimmune assay data

Design Comorbidity Portfolios to Improve Treatment Cost Prediction of Asthma Using Machine Learning

Comorbidity is an important factor to consider when trying to predict the cost of treating asthma patients. When an asthmatic patient suffered from comorbidity, the cost of treating such a patient becomes dependent on the nature of the comorbidity. Therefore, lack of recognition of comorbidity on asthmatic patient poses a challenge in predicting the cost of treatment. In this study, we proposed a comorbidity portfolio design that improves the prediction cost of treating asthmatic patients by regrouping frequently occurred comorbidities in different cost groups. In the experiment, predictive models, including logistic regression, random forest, support vector machine, classification regression tree, and backpropagation neural network were trained with real-world data of asthmatic patients from 2012 to 2014 in a large city of China. The 10-fold cross validation and random search algorithm were employed to optimize the hyper-parameters. We recorded significant improvements using our model, which are attributed to comorbidity portfolios in area under curve (AUC) and sensitivity increase of 46.89% (standard deviation: 4.45%) and 101.07% (standard deviation: 44.94%), respectively. In risk analysis of comorbidity on cost, respiratory diseases with a cumulative proportion in the adjusted odds ratio of 36.38% (95%CI: 27.61%, 47.86%) and circulatory diseases with a cumulative proportion in the adjusted odds ratio of 23.83% (95%CI: 15.95%, 35.22%) are the dominant risks of asthmatic patients that affects the treatment cost. It is found that the comorbidity portfolio is robust, and provides a better prediction of the high-cost of treating asthmatic patients. The preliminary characterization of the joint risk of multiple comorbidities posed on cost are also reported. This study will be of great help in improving cost prediction and comorbidity management.

Comparing the performance of meta-classifiers-a case study on selected imbalanced data sets relevant for prediction of liver toxicity

Abstract

Cheminformatics datasets used in classification problems, especially those related to biological or physicochemical properties, are often imbalanced. This presents a major challenge in development of in silico prediction models, as the traditional machine learning algorithms are known to work best on balanced datasets. The class imbalance introduces a bias in the performance of these algorithms due to their preference towards the majority class. Here, we present a comparison of the performance of seven different meta-classifiers for their ability to handle imbalanced datasets, whereby Random Forest is used as base-classifier. Four different datasets that are directly (cholestasis) or indirectly (via inhibition of organic anion transporting polypeptide 1B1 and 1B3) related to liver toxicity were chosen for this purpose. The imbalance ratio in these datasets ranges between 4:1 and 20:1 for negative and positive classes, respectively. Three different sets of molecular descriptors for model development were used, and their performance was assessed in 10-fold cross-validation and on an independent validation set. Stratified bagging, MetaCost and CostSensitiveClassifier were found to be the best performing among all the methods. While MetaCost and CostSensitiveClassifier provided better sensitivity values, Stratified Bagging resulted in high balanced accuracies.

Graphical Abstract

Electronic supplementary material

The online version of this article (10.1007/s10822-018-0116-z) contains supplementary material, which is available to authorized users.