Predictive data analytics for contract renewals: a decision support tool for managerial decision-making

Simsek, Serhat; Albizri, Abdullah; Johnson, Marina; Custis, Tyler; Weikert, Stephan

doi:10.1108/jeim-12-2019-0375

Cited by 24 publications

(21 citation statements)

References 37 publications

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“…The RF algorithm uses the DT algorithm to build numerous uncorrelated DTs by sampling observations with replacements from the training dataset (Hastie et al, 2017 ; James et al, 2013 ). Then, the individual DTs are combined using a function such as simple averages or majority voting (Johnson et al, 2020 , 2021 ; Simsek et al, 2020 ). Since RF uses multiple uncorrelated DTs by sampling the training set, it provides lower model variance and better accuracy rates, which are considered very robust.…”

Section: Methodology and Applicationmentioning

confidence: 99%

“…SVM is a supervised learning algorithm mainly used for classification. SVM uses quadratic programming to find hyperplanes that can optimally separate classes with the largest gap possible (Hastie et al, 2009 ; Simsek et al, 2020 ). It is important to note that SVM can utilize various kernel functions to classify datasets that are not linearly separable.…”

Section: Methodology and Applicationmentioning

confidence: 99%

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An AI-based Decision Support System for Predicting Mental Health Disorders

et al. 2022

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Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants’ answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.

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Section: Methodology and Applicationmentioning

confidence: 99%

Section: Methodology and Applicationmentioning

confidence: 99%

An AI-based Decision Support System for Predicting Mental Health Disorders

et al. 2022

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“…The support vector machine (SVM) has become very popular because it offers significant accuracy with minimal computational power. Support vector machines perform reasonably well with linear dependencies, have reasonable performance with sparse data sets, and can be used for a wide variety of data types [6]. The purpose of the support vector machine algorithm is to produce a hyperplane capable of differentiating between two dif-ferent classes of data.…”

Section: Support Vector Machinementioning

confidence: 99%

“…Absenteeism has generally been considered a significant challenge in human resource management in various industries and organizations [5]. Artificial intelligence and predictive analytics are perceived as key drivers in improving organizational performance and productivity [6]. In this study, we explore those factors that are closely related to absenteeism at work with a high degree of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Incorporating a Machine Learning Model into a Web-Based Administrative Decision Support Tool for Predicting Workplace Absenteeism

et al. 2022

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Productivity losses caused by absenteeism at work cost U.S. employers billions of dollars each year. In addition, employers typically spend a considerable amount of time managing employees who perform poorly. By using predictive analytics and machine learning algorithms, organizations can make better decisions, thereby increasing organizational productivity, reducing costs, and improving efficiency. Thus, in this paper we propose hybrid optimization methods in order to find the most parsimonious model for absenteeism classification. We utilized data from a Brazilian courier company. In order to categorize absenteeism classes, we preprocessed the data, selected the attributes via multiple methods, balanced the dataset using the synthetic minority over-sampling method, and then employed four methods of machine learning classification: Support Vector Machine (SVM), Multinomial Logistic Regression (MLR), Artificial Neural Network (ANN), and Random Forest (RF). We selected the best model based on several validation scores, and compared its performance against the existing model. Furthermore, project managers may lack experience in machine learning, or may not have the time to spend developing machine learning algorithms. Thus, we propose a web-based interactive tool supported by cognitive analytics management (CAM) theory. The web-based decision tool enables managers to make more informed decisions, and can be used without any prior knowledge of machine learning. Understanding absenteeism patterns can assist managers in revising policies or creating new arrangements to reduce absences in the workplace, financial losses, and the probability of economic insolvency.

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“…The support vector machine has become very popular because it offers significant accuracy with minimal computation power. Support vector machine (SVM) performs reasonably well with linear dependencies, have reasonable performance with sparse data sets, and can be used for a wide variety of data types (Simsek et al, 2021). The purpose of the support vector machine algorithm is to produce a hyperplane capable of differentiating between two different classes of data.…”

Section: Support Vector Machinementioning

confidence: 99%

Integration of a machine learning model into a decision support tool to predict absenteeism at work of prospective employees

Nath¹,

Harfouche²,

Coursey³

et al. 2022

Preprint

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Purpose -Inefficient hiring may result in lower productivity and higher training costs. Productivity losses caused by absenteeism at work cost U.S. employers billions of dollars each year. Also, employers typically spend a considerable amount of time managing employees who perform poorly. The purpose of this study is to develop a decision support tool to predict absenteeism among potential employees. Design/methodology/approach -We utilized a popular open-access dataset. In order to categorize absenteeism classes, the data have been preprocessed, and four methods of machine learning classification have been applied: Multinomial Logistic Regression (MLR), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forests (RF). We selected the best model, based on several validation scores, and compared its performance against the existing model; we then integrated the best model into our proposed web-based for hiring managers. Findings -A web-based decision tool allows hiring managers to make more informed decisions before hiring a potential employee, thus reducing time, financial loss and reducing the probability of economic insolvency. Originality/value -In this paper, we propose a model that is trained based on attributes that can be collected during the hiring process. Furthermore, hiring managers may lack experience in machine learning or do not have the time to spend developing machine learning algorithms. Thus, we propose a web-based interactive tool that can be used without prior knowledge of machine learning algorithms.

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Predictive data analytics for contract renewals: a decision support tool for managerial decision-making

Cited by 24 publications

References 37 publications

An AI-based Decision Support System for Predicting Mental Health Disorders

An AI-based Decision Support System for Predicting Mental Health Disorders

Incorporating a Machine Learning Model into a Web-Based Administrative Decision Support Tool for Predicting Workplace Absenteeism

Integration of a machine learning model into a decision support tool to predict absenteeism at work of prospective employees

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