ABSTRACT

The U.S. military has a continued interest in the development of handheld, field-usable sensors and test kits for a variety of diagnostic applications, such as traumatic brain injury (TBI) and infectious diseases. Field-use presents unique challenges for biosensor design, both for the readout unit and for the biological assay platform. We have developed robust biosensor devices that offer ultra-high sensitivity and also meet field-use needs. The systems under development include a multiplexed quantitative lateral flow test strip for TBI diagnostics, a field test kit for the diagnosis of pathogens endemic to the Middle East, and a microfluidic assay platform with a label-free reader for performing complex biological automated assays in the field.

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This project examines the effectiveness of applying machine learning techniques to the realm of college student success, specifically with the intent of discovering and identifying those student characteristics and factors that show the strongest predictive capability with regards to successful graduation. The student data examined consists of first time freshmen and transfer students who matriculated at California State University San Marcos in the period of Fall 2000 through Fall 2010 and who either graduated successfully or discontinued their education. Operating on over 30,000 student observations, random forests are used to determine the relative importance of the student characteristics with genetic algorithms to perform feature selection and pruning. To improve the machine learning algorithm cross validated hyperparameter tuning was also implemented. Overall predictive strength is relatively high as measured by the Matthews Correlation Coefficient, and both intuitive and novel features which provide support for the learning model are explored.