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Book part
Publication date: 30 September 2020

Suryakanthi Tangirala

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The…

Abstract

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive collection of clinical data by health care systems and treatment canters can be productively used to perform predictive analytics on treatment plans to improve patient health outcomes. These massive data sets have stimulated opportunities to adapt computational algorithms to track and identify target areas for quality improvement in health care.

According to a report from Association of American Medical Colleges, there will be an alarming gap between demand and supply of health care work force in near future. The projections show that, by 2032 there is will be a shortfall of between 46,900 and 121,900 physicians in US (AAMC, 2019). Therefore, early prediction of health care risks is a demanding requirement to improve health care quality and reduce health care costs. Predictive analytics uses historical data and algorithms based on either statistics or machine learning to develop predictive models that capture important trends. These models have the ability to predict the likelihood of the future events. Predictive models developed using supervised machine learning approaches are commonly applied for various health care problems such as disease diagnosis, treatment selection, and treatment personalization.

This chapter provides an overview of various machine learning and statistical techniques for developing predictive models. Case examples from the extant literature are provided to illustrate the role of predictive modeling in health care research. Together with adaptation of these predictive modeling techniques with Big Data analytics underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

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Book part
Publication date: 4 December 2020

Gauri Rajendra Virkar and Supriya Sunil Shinde

Predictive analytics is the science of decision-making that eliminates guesswork out of the decision-making process and applies proven scientific procedures to find right…

Abstract

Predictive analytics is the science of decision-making that eliminates guesswork out of the decision-making process and applies proven scientific procedures to find right solutions. Predictive analytics provides ideas on the occurrences of future downtimes and rejections thereby aids in taking preventive actions before abnormalities occur. Considering these advantages, predictive analytics is adopted in various diverse fields such as health care, finance, education, marketing, automotive, etc. Predictive analytics tools can be used to predict various behaviors and patterns, thereby saving the time and money of its users. Many open-source predictive analysis tools namely R, scikit-learn, Konstanz Information Miner (KNIME), Orange, RapidMiner, Waikato Environment for Knowledge Analysis (WEKA), etc. are freely available for the users. This chapter aims to reveal the best accurate tools and techniques for the classification task that aid in decision-making. Our experimental results show that no specific tool provides the best results in all scenarios; rather it depends upon the datasets and the classifier.

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Book part
Publication date: 5 October 2018

Nima Gerami Seresht, Rodolfo Lourenzutti, Ahmad Salah and Aminah Robinson Fayek

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes…

Abstract

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

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Fuzzy Hybrid Computing in Construction Engineering and Management
Type: Book
ISBN: 978-1-78743-868-2

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Article
Publication date: 2 November 2012

William McCluskey, Peadar Davis, Martin Haran, Michael McCord and David McIlhatton

The aim of this paper is to investigate the comparative performance of an artificial neural network (ANN) and several multiple regression techniques in terms of their…

Abstract

Purpose

The aim of this paper is to investigate the comparative performance of an artificial neural network (ANN) and several multiple regression techniques in terms of their predictive accuracy and capability of being used within the mass appraisal industry.

Design/methodology/approach

The methodology first tested that the data set had neglected non‐linearity which suggested that a non‐linear modelling technique should be applied. Given the capability of ANNs to model non‐linear data, this technique was used along with an OLS regression model (baseline model) and two non‐linear multiple regression techniques. In addition, the models were evaluated in terms of predictive accuracy and their capability of use within the mass appraisal environment.

Findings

Previous studies which have compared the predictive performance of an ANN model against multiple regression techniques are inconclusive. Having superior predictive capability is important but equally important is whether the technique can be successfully employed for the mass appraisal of residential property. This research found that a non‐linear regression model had higher predictive accuracy than the ANN. Also the output of the ANN was not sufficiently transparent to provide an unambiguous appraisal model upon which predicted values could be defended against objections.

Research limitations/implications

The research provides an informative view as to the efficacy of ANN methodology within the real estate field. A number of issues have been raised on the applicability of ANN models within the mass appraisal environment.

Practical implications

This work demonstrates that ANNs whilst useful as a predictive tool have a limited practical role for the assessment of residential property values for property tax purposes.

Originality/value

The work has taken forward the debate on the usefulness of ANN techniques within the mass appraisal environment.

Details

Journal of Financial Management of Property and Construction, vol. 17 no. 3
Type: Research Article
ISSN: 1366-4387

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Article
Publication date: 19 February 2020

Shashidhar Kaparthi and Daniel Bumblauskas

The after-sale service industry is estimated to contribute over 8 percent to the US GDP. For use in this considerably large service management industry, this article…

Abstract

Purpose

The after-sale service industry is estimated to contribute over 8 percent to the US GDP. For use in this considerably large service management industry, this article provides verification in the application of decision tree-based machine learning algorithms for optimal maintenance decision-making. The motivation for this research arose from discussions held with a large agricultural equipment manufacturing company interested in increasing the uptime of their expensive machinery and in helping their dealer network.

Design/methodology/approach

We propose a general strategy for the design of predictive maintenance systems using machine learning techniques. Then, we present a case study where multiple machine learning algorithms are applied to a particular example situation for an illustration of the proposed strategy and evaluation of its performance.

Findings

We found progressive improvements using such machine learning techniques in terms of accuracy in predictions of failure, demonstrating that the proposed strategy is successful.

Research limitations/implications

This approach is scalable to a wide variety of applications to aid in failure prediction. These approaches are generalizable to many systems irrespective of the underlying physics. Even though we focus on decision tree-based machine learning techniques in this study, the general design strategy proposed can be used with all other supervised learning techniques like neural networks, boosting algorithms, support vector machines, and statistical methods.

Practical implications

This approach is applicable to many different types of systems that require maintenance and repair decision-making. A case is provided for a cloud data storage provider. The methods described in the case can be used in any number of systems and industrial applications, making this a very scalable case for industry practitioners. This scalability is possible as the machine learning techniques learn the correspondence between machine conditions and outcome state irrespective of the underlying physics governing the systems.

Social implications

Sustainable systems and operations require allocating and utilizing resources efficiently and effectively. This approach can help asset managers decide how to sustainably allocate resources by increasing uptime and utilization for expensive equipment.

Originality/value

This is a novel application and case study for decision tree-based machine learning that will aid researchers in developing tools and techniques in this area as well as those working in the artificial intelligence and service management space.

Details

International Journal of Quality & Reliability Management, vol. 37 no. 4
Type: Research Article
ISSN: 0265-671X

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Abstract

Details

Applying Partial Least Squares in Tourism and Hospitality Research
Type: Book
ISBN: 978-1-78756-700-9

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Article
Publication date: 1 March 2001

Stuart Cooper, David Crowther and Chris Carter

This article considers the role of accounting in organisational decision making. It challenges the rational nature of decisions made in organisations through the use of…

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2179

Abstract

This article considers the role of accounting in organisational decision making. It challenges the rational nature of decisions made in organisations through the use of accounting models and the problems of predicting the future through the use of such models. The use of accounting in this manner is evaluated from an epochal postmodern stance. Issues raised by chaos theory and the uncertainty principle are used to demonstrate problems with the predictive ability of accounting models. The authors argue that any consideration of the predictive value of accounting needs to change to incorporate a recognition of the turbulent external environment, if it is to be of use for organisational decision making. Thus it is argued that the role of accounting as a mechanism for knowledge creation regarding the future is fundamentally flawed. We take this as a starting‐point to argue for the real purpose of the use of the predictive techniques of accounting, using its ritualistic role in the context of myth creation to argue for the cultural benefits of the use of such flawed techniques.

Details

Management Decision, vol. 39 no. 2
Type: Research Article
ISSN: 0025-1747

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Article
Publication date: 13 May 2021

James Mutuota Wakiru, Liliane Pintelon, Peter Muchiri and Peter Chemweno

The present study empirically compares maintenance practices under asset performance management (APM), employed by firms in developed and developing countries (Belgium and…

Abstract

Purpose

The present study empirically compares maintenance practices under asset performance management (APM), employed by firms in developed and developing countries (Belgium and Kenya, respectively).

Design/methodology/approach

Empirical observations and theoretical interpretations on maintenance practices under APM are delineated. A comparative cross-sectional survey study is conducted through an online questionnaire with 151 respondents (101 Kenya, 50 Belgium). Descriptive statistics and inferential statistics like independent t-test and phi coefficient were used for analyzing the data.

Findings

In both countries, reduction of maintenance and operational budget, return on assets, asset ageing and compliance aspects were established as critical factors influencing the implementation of asset maintenance and performance management (AMPM). A significant difference in staff competence in managing vibration, ultrasound and others like predictive algorithms was found to exist between the firms of the two countries. The majority of firms across the divide utilize manual and computer-based tools to integrate and analyse various maintenance data sets, while standardization and maintenance knowledge loss were found to adversely affect maintenance data management.

Research limitations/implications

The study findings are based on the limited number of returned responses of the survey questionnaire and focused on only two countries representing developed and developing economies. This study not only provides practitioners with the practical guidelines for benchmarking, but also induces the need to improve the asset maintenance strategies and data application practices for asset performance management.

Practical implications

The paper provides insights to researchers and practitioners in the articulation of imperative effective maintenance strategies, benchmarking and challenges in their implementation, considering the different operational context.

Originality/value

The paper contributes to theory and practice within the field of AMPM where no empirical research comparing developed and developing countries exist.

Details

International Journal of Quality & Reliability Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0265-671X

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Content available
Article
Publication date: 2 April 2019

Abdel Latef M. Anouze and Imad Bou-Hamad

This paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.

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4041

Abstract

Purpose

This paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.

Design/methodology/approach

Different statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their statistical counterpart, logistic regression.

Findings

The results showed that random forests and bagging outperform other methods in terms of predictive power.

Originality/value

This is the first study to assess the impact of environmental factors on banking performance in Middle East and North Africa countries.

Details

International Journal of Islamic and Middle Eastern Finance and Management, vol. 12 no. 2
Type: Research Article
ISSN: 1753-8394

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Article
Publication date: 13 March 2009

Ranjit Bose

Advanced analytics‐driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such…

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10955

Abstract

Purpose

Advanced analytics‐driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics.

Design/methodology/approach

A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice.

Findings

The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses.

Originality/value

The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.

Details

Industrial Management & Data Systems, vol. 109 no. 2
Type: Research Article
ISSN: 0263-5577

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