New Machine Learning Study Results Reported from St. Louis University (A Cluster-based Machine Learning Ensemble Approach for Geospatial Data: Estimation of Health Insurance Status In Missouri)

2019 MAR 06 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators publish new report on Machine Learning. According to news reporting out of St. Louis, Missouri, by NewsRx editors, research stated, “Mainstream machine learning approaches to predictive analytics consistently prove their ability to perform well using a variety of datasets, although the task of identifying an optimally-performing machine learning approach for any given dataset becomes much less intuitive. Methods such as ensemble and transformation modeling have been developed to improve upon individual base learners and datasets with large degrees of variance.”

Financial support for this research came from Saint Louis University through the GeoSLU Big Idea Grant.

Our news journalists obtained a quote from the research from St. Louis University, “Despite the increased generalizability and flexibility of ensemble approaches, the cost often involves sacrificing inference for predictive ability. This paper introduces an alternative approach to ensemble modeling, combining the predictive ability of an ensemble framework with localized model construction through the incorporation of cluster analysis as a pre-processing technique. The workflow not only outperforms independent base learners and comparative ensemble methods, but also preserves local inferential capability by manipulating cluster parameters and maintaining interpretable relative importance values and non-transformed coefficients for the overall consideration of variable importance.”

According to the news editors, the research concluded: “This paper demonstrates the ensemble technique on a dataset to estimate rates of health insurance coverage across the state of Missouri, where the cluster pre-processing assists in understanding both local and global variable importance and interactions when predicting high concentration areas of low health insurance coverage based on demographic, socioeconomic, and geospatial variables.”

For more information on this research see: A Cluster-based Machine Learning Ensemble Approach for Geospatial Data: Estimation of Health Insurance Status In Missouri. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2019;8(1):. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION can be contacted at: Mdpi, St Alban-Anlage 66, Ch-4052 Basel, Switzerland.

Our news journalists report that additional information may be obtained by contacting E. Mueller, St. Louis University, Coll Arts & Sci, Integrated & Appl Sci Bioinformat & Geospatial Bi, St Louis, MO 63103, United States. Additional authors for this research include J.S.O. Sandoval, S. Mudigonda and M. Elliott.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.3390/ijgi8010013. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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