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Heart failure (HF) has a highly variable annual mortality rate and there is an urgent need of determining patient prognosis to enable informed decision-making about heart failure treatment strategies. Existing survival risk prediction models either require features that limit their applicability or pose difficulties for parameter estimation as physicians have to use a limited set of variables with...
Identifying future high-cost patients allows healthcare organizations to take preventative measures to both reduce future patient costs and lessen the burden of illness. This paper expands upon past risk adjustment strategies to predict the persistently high-cost patients by combining clinical and claims data on patients and assessing risk using machine learning techniques. Our approach not only leads...
In the multiple target tracking scenarios, the correct matching between targets and measurements is critical. There have been many approaches to resolve this problem called data association. In this paper, a regression method is proposed to resolve the data association problem. In the logistic regression model, nine potential predictor variables are designed which are related to the geometric information...
Titanic disaster occurred 100 years ago on April 15, 1912, killing about 1500 passengers and crew members. The fateful incident still compel the researchers and analysts to understand what can have led to the survival of some passengers and demise of the others. With the use of machine learning methods and a dataset consisting of 891 rows in the train set and 418 rows in the test set, the research...
The aim of the project is to develop a Machine Learning model to perform predictive analytics on the banking dataset. The banking data set consists of details about customers like and whether the customer will buy a product provided by the bank or not. The data set is obtained from University of California Irvine Machine Learning Repository. This data set is used to create a binary classification...
Fishery industry has been developed rapidly in recent years. To establish an accurate and automatic fishery forecast system becomes concern of industrialists and even researchers. Existing fisheries forecast approaches mainly rely on typical common-used classification models. However, there still not exists systematical evaluation and empirical research for these approaches, which leads to application...
Large-scale Hierarchical Classification (HC) involves datasets consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-scale HC problem. It speeds up the training process, reduces the prediction time and...
Predicting microblog user retweet behaviors is the basis of building the information diffusion model in microblog social networks. In order to improve the accuracy of predicting user retweet behaviors, under the MRF (Markov Random Field) framework, the paper comprehensively analyzes the effects on user retweet behaviors caused by various features (e.g., user attributes and microblog contents) and...
Despite the decline in mortality rates for extremely preterm infants, intraventricular haemorrhage (IVH) remains a threat to their survival. In this study, we sought to explore logistic regression models for predicting IVH as they would be applied in a clinical setting, using features derived from respiratory and blood pressure signals. Calculated predictors included mean (μ) and the short- and long-term...
In Machine Learning, we often encounter instances of imbalanced data which occur whenever there is an unequal representation in the classification categories. New found interest in Machine Learning has made its usage ubiquitous. Its applications encompass a wide plethora of scenarios ranging from Business and Banking to Bioinformatics and Psychology. These problems are often characterized by imbalanced...
Large-scale classification of data where classes are structurally organized in a hierarchy is an important area of research. Top-down approaches that exploit the hierarchy during the learning and prediction phase are efficient for large-scale hierarchical classification. However, accuracy of top-down approaches is poor due to error propagation i.e., prediction errors made at higher levels in the hierarchy...
Sequencing arrival flights is a major task of air traffic management, and there exist various optimization tools to support the air traffic controllers. It is, however, difficult to employ these tools in the actual operational environments since they lack consideration on the human cognitive process. This paper proposes a new framework to predict the arrival sequences based on a preference learning...
Traditional marketing approach mainly adopts advertising and telemarketing method to attract potential consumers. It will need a lot of manpower and resources, but cannot position the targeted consumers accurately. In this paper, a novel precision automotive marketing model based on telecom big data mining is proposed to predict the potential high-end luxury car buyers. Initially, both logistic regression...
Three scoring systems were considered and described for dermatological applications in which malignant and benign skin lesions have to be recognized: two models are derived from logistic regression and naïve Bayes rule by rounding model parameters to their nearest integer values; the third approach defines the scoring system by a direct stepwise adding of the most significant binary risk factors....
The main goal of the work presented in this paper was to develop a set of algorithms which allows to predict what will be the probability ratio of acquisition of the items form the given database. To fulfill this goal, the appropriate statistical methods were developed, mainly using R programming language. In order to apply the specific statistical methods, the appropriate database preprocessing was...
Telehealth provides an opportunity to reduce healthcare costs through remote patient monitoring, but is not appropriate for all individuals. Our goal was to identify the patients for whom telehealth has the greatest impact, as measured through cost savings and patient engagement. For prediction of cost savings, challenges included the high variability of medical costs and the effect of selection bias...
in areas with rapid economic growth, distribution transformer heavy load and overload occur frequently, which may damage the equipment and even lead to power outages. Therefore, it is critical for the utilities to know which distribution transformers are more likely to have the heavy load /overload conditions in the next year in order to facilitate asset management in distribution network. However,...
XAPPmedia provides an interactive audio advertising service that allows customers to connect with advertisers by speaking prompted phases in their audio advertisement. In order to provide better services for their advertisers, XAPPmedia needs to determine key components that influence advertising performance. We developed an Ad Effectiveness model that coaches advertisers to optimize their XAPP advertising...
Patient acquisition of carbapenem resistant bacteria in hospitals is a serious problem that leads to adverse outcomes for infected patients. The most common carbapenem resistance mechanism in US hospitals is a mobile gene called Klebsiella pneumoniae carbapenemase (KPC), which can move between bacterial species. Previous research has demonstrated that patient-to-patient transmission cannot fully account...
Predicting failures in a distributed system based on previous events through logistic regression is a standard approach in literature. This technique is not reliable, though, in two situations: in the prediction of rare events, which do not appear in enough proportion for the algorithm to capture, and in environments where there are too many variables, as logistic regression tends to over fit on this...
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