Geographic Information Systems and Science

Geographic information science (GISc) has established itself as a collaborative information-processing scheme that is increasing in popularity. Yet, this interdisciplinary and/or transdisciplinary system is still somewhat misunderstood. This book talks about some of the GISc domains encompassing students, researchers, and common users. Chapters focus on important aspects of GISc, keeping in mind the processing capability of GIS along with the mathematics and formulae involved in getting each solution. The book has one introductory and eight main chapters divided into five sections. The first section is more general and focuses on what GISc is and its relation to GIS and Geography, the second is about location analytics and modeling, the third on remote sensing data analysis, the fourth on big data and augmented reality, and, finally, the fifth looks over volunteered geographic information.info:eu-repo/semantics/publishedVersio

Development of a Recommender System for Dental Care Using Machine Learning

Resource mismanagement along with the underutilization of dental care has led to serious health and economic consequences. Artificial intelligence was applied to a national health database to develop recommendations for dental care. The data were obtained from the 2013–2014 National Health and Nutrition Examination Survey to perform machine learning. Feature selection was done using LASSO in R to determine the best regression model. Prediction models were developed using several supervised machine learning algorithms, including logistic regression, support vector machine, random forest, and classification and regression tree. Feature selection by LASSO along with the inclusion of additional clinically relevant variables identified 8 top features associated with recommendation for dental care. The top 3 features include gum health, number of prescription medications taken, and race. Gum health shows a significantly higher relative importance compared to other features. Demographics, healthcare access, and general health variables were identified as top features related to receiving additional dental care, consistent with prior research. Practicing dentists and other healthcare professionals can follow this model to enable precision dentistry through the incorporation of our algorithms into computerized screening tool or decision tree diagram to achieve more efficient and personalized preventive strategies and treatment protocols in dental care