Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease.

Since 2001, cardiovascular disease (CVD) has had the second-highest mortality rate, about 15,700 people per year, in Taiwan. It has thus imposed a substantial burden on medical resources. This study was triggered by the following three factors.

An Advanced ICTVSS Model for Real-Time Vehicle Traffic Applications.

From the accident news, it is found that the occurrences of traffic accidents every year and the numbers of deaths and injuries have raised continually and have become a specific issue concerned in society in Taiwan. More seriously, the number of traffic accidents is positively increased with the increasing motorized vehicles. Thus, to reduce the incidence of traffic accidents through by some advanced real-time technologies is an important and interesting work.

A comprehensive identification-evidence based alternative for HIV/AIDS treatment with HAART in the healthcare industries.

Background And Objective: The HIV/AIDS-related issue has given rise to a priority concern in which potential new therapies are increasingly highlighted to lessen the negative impact of highly active anti-retroviral therapy (HAART) in the healthcare industry. With the motivation of "medical applications," this study focuses on the main advanced feature selection techniques and classification approaches that reflect a new architecture, and a trial to build a hybrid model for interested parties.

Methods: This study first uses an integrated linear-nonlinear feature selection technique to identify the determinants influencing HAART medication and utilizes organizations of different condition-attributes to generate a hybrid model based on a rough set classifier to study evolving HIV/AIDS research in order to improve classification performance.

An empirical study of a hybrid imbalanced-class DT-RST classification procedure to elucidate therapeutic effects in uremia patients.

The high prevalence and incidence of severe renal diseases exhaust constrained medical resources for the treatment of uremia patients. In addition, the problem of imbalanced-class data distributions induces negative effects on classifier learning algorithms. Hemodialysis is the most common treatment for uremia diseases due to the limited supply of donated organs available for transplantation.

A Machine Learning Method for Power Prediction on the Mobile Devices.

Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption of running processes with respect to system-call patterns are not well studied. In this paper, we propose a fuzzy neural network (FNN) for training and analyzing process execution behaviour with respect to series of system calls, parameters and their power consumptions.

Evolving MCDM applications using hybrid expert-based ISM and DEMATEL models: an example of sustainable ecotourism.

Ecological degradation is an escalating global threat. Increasingly, people are expressing awareness and priority for concerns about environmental problems surrounding them. Environmental protection issues are highlighted.

Modeling hybrid rough set-based classification procedures to identify hemodialysis adequacy for end-stage renal disease patients.

Healthcare problems observed in the majority of end-stage renal disease (ESRD) patients regarding hemodialysis (HD) treatment are serious issues for the Taiwanese healthcare services, and an interesting topic is thus the adequacy of HD therapy. This study successfully models a hybrid procedure to measure HD adequacy to assess therapeutic effects and to explore the relationship between accuracy and coverage for interested parties. The proposed model has better accuracy, a lower standard deviation, and fewer attributes than the listed methods under various evaluation criteria.

Identifying the medical practice after total hip arthroplasty using an integrated hybrid approach.

A critical option of total hip arthroplasty (THA) is considered only when tried more conservative treatments but continued to have pain, stiffness, or problems with the function of ones hip. THA plays one of major concerns under the waves of the rapid growth of aging populations and the constrained health care resources in Taiwan. Moreover, prior studies indicated that imbalanced class distribution problems do exist in the constructed classification model and cause seriously negative effects on model performances in the health care industry.

Identifying patients in target customer segments using a two-stage clustering-classification approach: a hospital-based assessment.

Identifying patients in a Target Customer Segment (TCS) is important to determine the demand for, and to appropriately allocate resources for, health care services. The purpose of this study is to propose a two-stage clustering-classification model through (1) initially integrating the RFM attribute and K-means algorithm for clustering the TCS patients and (2) then integrating the global discretization method and the rough set theory for classifying hospitalized departments and optimizing health care services. To assess the performance of the proposed model, a dataset was used from a representative hospital (termed Hospital-A) that was extracted from a database from an empirical study in Taiwan comprised of 183,947 samples that were characterized by 44 attributes during 2008.