Assessing positive matrix factorization model fit: a new method to estimate uncertainty and bias in factor contributions at the daily time scale

International audienceA Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM2.5 concentrations, comprised of 39 chemical species from nine pollutant sources. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A balanced bootstrap is used to create replicate datasets, with the same model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series, and this classification is used to align factor orderings across results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input factors used to create the synthetic data assesses bias. The results indicate that variability in factor contribution estimates does not necessarily encompass model error: contribution estimates can have small associated variability yet also be very biased. These results are likely dependent on characteristics of the data

Kidney Ailment Prediction under Data Imbalance

Chronic Kidney Disease (CKD) is the leading cause for kidney failure. It is a global health problem affecting approximately 10% of the world population and about 15% of US adults. Chronic Kidney Diseases do not generally show any disease specific symptoms in early stages thus it is hard to detect and prevent such diseases. Early detection and classification are the key factors in managing Chronic Kidney Diseases. In this thesis, we propose a new machine learning technique for Kidney Ailment Prediction. We focus on two key issues in machine learning, especially in its application to disease prediction. One is related to class imbalance problem. This occurs when at least one of the classes are represented by significantly smaller number of samples than the others in the training set. The problem with imbalanced dataset is that the classifiers tend to classify all samples as majority class, ignoring the minority class samples. The second issue is on the specific type of data to be used for a given problem. Here, we focused on predicting kidney diseases based on patient information extracted from laboratory and questionnaire data. Most recent approaches for predicting kidney diseases or other chronic diseases rely on the usage of prescription drugs. In this study, we focus on biomarker and anthropometry data of patients to analyze and predict kidney-related diseases. In this research, we adopted a learning approach which involves repeated random data sub-sampling to tackle the class imbalance problem. This technique divides the samples into multiple sub-samples, while keeping each training sub-sample completely balanced. We then trained classification models on the balanced data to predict the risk of kidney failure. Further, we developed an intelligent fusion mechanism to combine information from both the biomarker and anthropometry data sets for improved prediction accuracy and stability. Results are included to demonstrate the performance