Use Of Decision Tree Analysis Research Articles

Assessing tuberculosis in the skeleton with the use of decision tree analysis.

Diagnosis of specific infectious diseases in the skeleton is often difficult and relies on expert opinion. Statistics is not often used as a tool to assist in such diagnoses, and therefore this study aimed at employing data mining and machine learning in the form of decision tree analysis to aid in recognizing tuberculosis (TB) in skeletal remains and find patterns of skeletal involvement. The sample included 387 modern South African individuals (n = 207 individuals known to have died of TB and n = 180 as a control group) which were scored for the presence or absence of 21 skeletal lesions documented to be associated with TB. A pruned decision tree classification analysis was done to detect significant patterns and associations between variables which produced a model with a moderate classification rate based on four of the variables. As expected, vertebral changes were selected first, followed by rib, acetabular and lastly cranial changes. As a proof of concept, it was shown that machine learning was able to identify patterns of changes in TB skeletons versus a control group. However, further investigation into the use of machine learning in assessing skeletal changes associated with specific diseases is needed.

Factors associated with fall risk of community-dwelling older people: A decision tree analysis.

To examine the predictive attributes for accidental falls in community-dwelling older people in Hong Kong using decision tree analysis. We recruited 1151 participants with an average age of 74.8 years by convenience sampling from a primary healthcare setting to carry out the cross-sectional study over 6 months. The whole dataset was divided into two sets, namely training set and test set, which respectively occupied 70% and 30% of the whole dataset. The training dataset was used first; decision tree analysis was used to identify possible stratifying variables that could help to generate separate decision models. The number of fallers was 230 with 20% 1-year prevalence. There were significant differences in gender, use of walking aids, presence of chronic diseases, and co-morbidities including osteoporosis, depression, and previous upper limb fractures, and performance in the Timed Up and Go test and the Functional Reach test among the baselines between the faller and non-faller groups. Three decision tree models for the dependent dichotomous variables (fallers, indoor fallers, and outdoor fallers) were generated, with overall accuracy rates of the models of 77.40%, 89.44% and 85.76%, respectively. Timed Up and Go, Functional Reach, body mass index, high blood pressure, osteoporosis, and number of drugs taken were identified as stratifying variables in the decision tree models for fall screening. The use of decision tree analysis for clinical algorithms for accidental falls in community-dwelling older people creates patterns for decision-making in fall screening, which also paves the way for utility-based decision-making using supervised machine learning in fall risk detection.

The use of decision tree analysis for improving age estimation standards from the acetabulum

Accurate, reliable and easy-to-use statistical methods in multifactorial age estimation from the skeleton remains a much-debated issue. In this paper, we explore the use of decision trees in adult age estimation. For this purpose, a dataset from 100 acetabula of South Africans, previously used for age estimation using transition analysis, were used to build a basic decision tree. A test sample of 25 individuals were then employed to assess the newly developed decision tree. Using the decision tree, 20 of the 25 individuals were classified into the correct age group (young, middle or older adults), with the remaining 5 falling within the adjacent age group. The decision tree provided a more accurate outcome as compared to the previous study using transition analysis. Although much research is still needed, this analysis suggests that decision trees may be usable in adult age estimation and may handle the non-linear relationship between chronological and biological age somewhat better than other traditional statistical methods.

Decision Tree Analysis for Law Practice

Decision trees can help lawyers better counsel clients and predict outcomes. They can serve as a bridge from indecipherable data to a better informed and counseled client. Marjorie Corman Aaron’s new book, Risk and Rigor: A Lawyer’s Guide to Decision Trees for Assessing Cases and Advising Clients, has the potential to help bring about more widespread and effective use of decision tree analysis in lawyering. Risk and Rigor makes a comprehensive contribution to decision tree analysis learning. This review proceeds in four parts. Part I provides an overview of how decision tree analysis can serve the lawyer, on one hand, and law school teachers and their students, on the other. Part II examines the nuts-and-bolts parts of the book where the author teaches the willing reader how decision trees work and how to build them. Part III engages with the author’s assessment of the risks and limitations of decision tree analysis. Part IV queries whether decision trees may pose a barrier to some. Risk and Rigor had me wondering if decision tree analysis is equally accessible and welcomed by all, or if there might be some barriers to using decision trees with clients and law students. Risk and Rigor leaves relatively unexplored whether or not educational, gender, racial, and other biases may compromise the effectiveness of the use of decision tree analysis as a means of cultivating better client communication and counseling. Ultimately, Risk and Rigor makes a persuasive case for the use of decision tree analysis. A practical, user-friendly addition to dispute resolution literature on decision analysis, Risk and Rigor challenges the reader to consider not just the benefits but also the limitations of decision trees.

Determinants of aphasia recovery in early post-stroke phase: Exploratory use of decision tree analysis (913)

Determinants of aphasia recovery in early post-stroke phase: Exploratory use of decision tree analysis (913)

Interrelated Factors for Return to Work of Sick-Listed Employees in Sweden

Abstract Purpose: Long-term sickness absence is a significant human and economic cost in many countries, including Sweden making research on factors which impact on return to work (RTW) relevant. This study has two aims: (1) provide an overview of factors that impact RTW expectations in a national sample of Swedish workers on long-term sickness absence; and (2) gain an understanding of the interrelationships among these factors using a socioecological framework and decision tree analysis. Method: A survey, designed to capture information about demographic variables, health and work ability, workplace contact, supervisor support and expectations of return to work, was mailed to 1,112 randomly selected sick-listed people in Sweden and completed by 534, representing a response rate of 48%. Results: The most important factors affecting RTW expectations were work ability and burnout. Employees reporting high levels of work ability were more likely to expect to RTW compared to those reporting low levels, and this was dependent on their relative burnout score. Those with a high burnout score were less likely to expect to RTW, while for those with a low burnout score RTW expectations were dependent on age, country of birth, and supervisor support. For young employees reporting low work ability and low burnout score, RTW expectations were lower. Conclusions: Our results suggest a more nuanced approach to delivery of RTW services is required, whereby practitioners need to understand the socioecology of the range of factors that impact RTW expectations. The use of decision tree analysis facilitates this understanding by describing the interrelationships between these factors.

Optimal evacuation decision policies for Benue flood disaster in Nigeria

This research develops a probabilistic evacuation decision policy model to determine the optimal decision for the cases of no evacuation, evacuation, and delay decision at a particular point in time in the event of a threatening flood disaster adapted to the case of Nigeria. This work explores the use of decision tree analysis and a program written in MATLAB to solve problems arising from the models to determine the optimal decision. An application to the case of Benue flood 2012 reveals that earlier evacuation would have saved the decision-maker (government) almost 4-times less than the cost incurred after evacuation was initiated at later time.

Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic-Ischemic Brain Injury.

Prognostication following hypoxic ischemic encephalopathy (brain injury) is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data. The inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003-2011). Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS), features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume) associates of severe disability and death. We used the area under the ROC (auROC) to determine accuracy of model. There were 41 (63.7% males) patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82-1.00). At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86-1.00). Our findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

Using decision trees to identify tourism stakeholders

PurposeThe purpose of this paper is to investigate the use of decision tree analysis in the identification of stakeholders based on their participation in tourism and political activities in a community.Design/methodology/approachA survey was sent to tourism stakeholders in two rural counties. Responses were collected and analyzed using the exhaustive chi‐square automatic interaction detection decision tree analysis.FindingsBased on the results of the decision tree analysis four tourism stakeholder groups were identified based on their participation in tourism and political activities in a community: high participants, high‐moderate participants, low‐moderate participants, and low participants.Research limitations/implicationsOwing to a low response rate, an issue of non‐response bias could exist, but the information from the respondents can give insight on stakeholders in these communities. Also, the specific results of this study can only be applied to eastern North Carolina and are not generalizable to other areas.Practical implicationsResults from this study demonstrate the use of decision tree analysis in identifying community stakeholders. Using decision tree analysis tourism planners can identify stakeholder groups that will participate in tourism and political activities. With this knowledge, tourism planners can identify which stakeholder groups will be the most influential and vocal in a community with regard to tourism development.Originality/valueDecision tree analysis is a tool for partitioning a data set based on the relationships between a set of independent variables and a dependent variable. The research reported here tests the application of decision tree analysis, an analytical technique that is not traditionally used to segment stakeholders in tourism.

Measuring the Resiliency of the Manhattan Points of Entry in the Face of Severe Disruption

Problem statement: Resilient infrastructure systems are able to continue to provide the expected service levels following disruptive events. Implementing resiliency in infrastructure systems requires knowledge of the current resiliency of the system and a methodology by which different resiliency strategies can be evaluated. In the transportation infrastructure in particular, disruptions cause delays, which will in turn incur substantial economic losses and environmental damages. Approach: The Networked Infrastructure Resiliency framework (NIRA) is proposes to assess the resiliency of the road network that connects Manhattan in New York City to the rest of the regions. The framework proposes to create a network model of the system onto which hypothetical disruptions can be introduced and then to measure resiliency as the impact of disruptions on the performance measures of the system. One of the key performance measures of the transportation infrastructure system is the travel time; hence, the base resiliency of the system is measured as the ratio of the travel time preceding a disruption to the travel time following a disruption. Different resiliency strategies that improve the system’s resiliency can be evaluated through the use of decision tree analysis. Results: The proposed NIRA framework is a novel approach for assessing the resiliency of networked infrastructure system by measuring the impact of disruptions on the system’s performance measures. In road transportation networks, such as that connecting Manhattan entry points, resiliency is achievable through reducing the vulnerability of the system and increasing its adaptive capacity. Conclusion: One vulnerability reduction strategy is the clever assignment of vehicles to other routes in the network. The adaptive capacity of the system is enhanced through the deployment of other parallel systems such as ferries.

Applied digital library project management

PurposeThis paper seeks to define and describe decision tree analysis as a method for decision making when there are multiple, linearly or hierarchically related decision points that must be considered when determining the optimum choices within a project.Design/methodology/approachUsing theory and example, the paper relates the use of decision tree analysis to the successful selection of decision points in complex, multistage digital library projects.FindingsDecision tree analysis is useful as a method for both determining the various courses of action within a project and visualizing these choices in an easily understood format. Decision tree analysis can help a team understand the relationships between issues in a project as well as the consequences of various courses of action.Originality/valueThe paper fills a gap in the digital library project management literature by providing an overview of a tool used in operational research that helps identify an optimal course of action within a complex system of interrelated decisions.

Decision tree analysis as a tool to optimise patent current awareness bulletins

Efficient setting up and maintenance of patent current awareness bulletins requires a good and constant dialogue between the information professionals and the end users. In view of giving the end users a chance to participate in the definition and optimisation of the patent bulletins, at the same time trying to simplify and rationalise as much as possible such a time consuming and labour-intensive task, the use of decision tree analysis was investigated, as a promising solution for determining a classification model capable of helping the information professional in assessing the desired level of detail to be assigned to each specific search profile.

Use of Decision Tree Analysis to Improve LDA Decision Making

In food animal practice, medical decisions are usually made on the basis of economic impact to the dairy, rather than on perceived individual animal value or emotional attachment. A decision routinely made on dairies is what to do with cows that develop left displaced abomasums (LDA). Given the variety of management options available, veterinarians have the potential to make different recommendations depending upon the cow's historical value, parity, current stage oflactation, presence of concurrent disease, level of milk price and relative replacement cost. Decision trees are systematic quantitative tools that may be used to improve the ability to select the best course of action in situations, such as LDA, where the clinical decision is complex and outcomes are uncertain. The objective of this project was to demonstrate the use of a decision tree model as a tool to help select between surgery, roll and toggle or marketing for beef as the most economically appropriate management plan, given an early lactation cow with an LDA.

Identification of critical batch operating parameters in fed-batch recombinant E. coli fermentations using decision tree analysis.

To develop a useful fermentation process model, it is first necessary to identify which batch operating parameters are critical in determining the process outcome. To identify critical processing inputs in large databases, we have explored the use of Decision Tree Analysis with the decision metrics of Gain (i.e., Shannon Entropy changes), Gain Ratio, and a multiple hypergeometric distribution. The usefulness of this approach lies in its ability to treat "categorical" variables, which are typical of archived fermentation databases, as well as "continuous" variables. In this work, we demonstrate the use of Decision Tree Analysis for the problem of optimizing recombinant green fluorescent protein production in E. coli. A database of 85 fermentations was generated to examine the effect of 15 process input parameters on final biomass yield, maximum recombinant protein concentration, and productivity. The use of Decision Tree Analysis led to a considerable reduction in the fermentation database through the identification of the significant as well as insignificant inputs. However, different decision metrics selected different inputs and different numbers of inputs to classify the data for each output.

Use of Decision Tree Analysis for Determination of Critical Enological and Viticultural Processing Parameters in Historical Databases

A method for optimizing wine quality or characteristics based on historical enological and viticultural data is being developed. The first step in this method is a database analysis and classification step where the most critical inputs to the process are determined. To identify critical processing inputs in large databases, we explored the use of decision tree analysis in conjunction with information theory for data classification. The success of this method lies in its ability to treat "categorical" variables (for example, vineyard, clone), which are typical of archived viticultural and enological data, as well as "continuous" variables (for example, temperature, sugar concentration). We have demonstrated that decision trees identify important variables using an enological database for which statistical analysis was previously completed. Results from both decision tree analysis and statistical analysis agree well. Both find the same subset of winemaking inputs in this database to be significant in classifying outputs such as tartaric acid concentration, total phenolic concentration, glycerol concentration, and malolactic fermentation duration. Decision tree analysis has also been tested successfully on an actual viticultural database from a winery to predict the effect of viticultural practices on wine quality, reducing a database of twenty inputs to six inputs that had a significant effect on a measure of wine quality. Partial Least Squares analysis has been explored as an alternative classification approach for databases containing only continuous variables, yielding comparable results to decision tree analysis. The final method developed will provide wineries with a straightforward means to identify their most important enological and viticultural practices and ensure production of a wine with the desired qualities.

Leptospirosis vaccination in beef cattle: Use of decision tree analysis

Decision tree analysis was used to evaluate the economic benefits of leptospirosis vaccination in beef cattle. The analysis used estimated values for the cost of vaccination, the probability of calf losses from leptospirosis and the economic losses resulting from calf mortality to calculate the expected value of vaccination. A computer spreadsheet was used to calculate the expected value of the vaccination programme over a wide range of estimated costs and probabilities and to determine the break-even point at which vaccination becomes economic. A sensitivity analysis demonstrated that the outcome of the analysis was most dependent on calf value and the probability of calf loss due to leptospirosis. The analysis was relatively insensitive to changes in vaccine cost, vaccine efficacy and herd fertility.

Decision trees for forecasting

AbstractRecent years have seen an increasing cross‐fertilization between the fields of decision analysis and forecasting. Decision‐analytic models often require forecasts as inputs, and aspects of the Bayesian decision‐theoretic framework underlying decision analysis have proved useful to forecasting, particularly in contexts where subjective judgemental inputs are required. This paper describes the use of decision tree analysis for forecasting and illustrates its use for corporate divisional forecasting and planning. A specialized decision‐analytic technique, acts as events, is also described and illustrated to forecast a new product's earnings. Conclusions are drawn about the applicability of decision analysis for forecasting.