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Heavy episodic drinking is a well-established risk factor for heart disease, diabetes, certain cancers, stroke, hypertension and injuries, however, little is known about whether health problems precipitate changes in subsequent drinking patterns. Retrospective cohort analyses of heavy drinking by decade were conducted using data from the 2010 U.S. National Alcohol Survey (n=5240). Generalized estimating...
In a Big Data era, a lot of open data set is published and shared with the public. That creates new services and business. However, the publication may cause a leakage problem of private information. In general, de-identification techniques are applied to the data before publication. The problem, however, has not been solved completely. Personal data can be obtained from the several sources such as...
Information fusion is a research domain that strives to establish theories that exploit and analyze the data retrieved from multiple sources. Generally, these fusion theories try to combine these data for a classification task and to make the decision efficiently. The possibility theory is one of the most known in the information fusion domain. So, the possibility distribution estimation step represents...
Diversity is deemed to be a key issue in classifier combination. For this reason, not every classifier is an expert for every query pattern. Thus, many researchers have focused on dynamic ensemble selection. Most works, however, use only one criterion to perform the dynamic selection. Hence, multiple criteria can provide a decision more effective than the one produced by any of the criteria. Another...
There is an increasing need to accurately and efficiently find relevant clinical trials for patients, practitioners, and researchers. This paper proposes a method for measuring the similarity among clinical trials and explores its potential uses in efficiently suggesting relevant clinical trials. SNOMED terms are applied to extract and normalize the clinical trial titles (CTTs). Similarity matrices...
Supervised learning based classification depends on learning from previously known data set. Here, these data sets governs training for classification of new data points. This training is mainly driven by two fundamental approaches. First one is derivative based approach and another centers around heuristics or direct search based methodologies. Both approaches have their pros and cons depending upon...
Rural areas of Bangladesh do not have quality healthcare facilities or doctors. However, Internet is widely available everywhere in the country. A web based Clinical Decision Support System (CDSS) is proposed that will serve the rural medical centers. A Clinical Decision Support System is a software that provides diagnostic suggestions based on input patient data. The system will use Artificial Intelligence...
The aim of this work is the development of a method for the automatic determination of the optimum number of base classifiers which consists of the Random Forests. The novelty of the proposed method is that it doesn't need to select the classifiers to be in the final ensemble from a pool of classifiers which is known in advance, but determines the number of classifiers dynamically during the growing...
It is often that the learned neural networks end with different decision boundaries under the variations of training data, learning algorithms, architectures, and initial random weights. Such variations are helpful in designing neural network ensembles, but are harmful for making unstable performances, i.e., large variances among different learnings. This paper discusses how to reduce such variances...
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