Evaluation of Wines Rating Based on Sensory Characteristics Using Neural Networks

Wine is an agricultural product with very high commerce price variation, which is highly affected by quality ratings. Therefore, quality rating is particularly important for both industry and consumers. However, absence of clear concepts on what constitutes wine quality makes the perception of quality highly subjective, and it is usual for tasters to disagree on the quality rating of a specific wine. For this purpose, a Feedforward Neural Network (FNN) could be trained in order to predict wine quality. In this study, a new FNN method was developed to predict the accurate wine quality based on major sensory characteristics as FNN inputs, and to improve the ability of a taster, groups of tasters, or consumers, to rate wine by taking into account previous decisions. Specifically, five principle sensory characteristics of wines were used as inputs (Aging in Barrel, Aroma Intensity, Body, Astringency, and Acidity) in a rating range 1-3. As outputs, the quality ratings of wines in a range 70-100 were considered. The FNN was created in MATLAB with 1 hidden layer, 5 neurons and 1 output layer. For ratings divided in 5 categories the accuracy was 53% with the use of the FNN, as opposed to the accuracy of 36% achieved by Multiple Linear Regression. For ratings divided in 9 categories the accuracy was 90%. This method may allow each individual or group of tasters to introduce their own data to produce a more objective rating by taking into account previous decisions (subjective) that have accumulated in the database

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future