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

Nath, Gopal; Wang, Yawei; Coursey, Austin; Saha, Krishna K.; Prabhu, Srikanth; Sengupta, Saptarshi

doi:10.3390/info13070320

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…Researchers have used ML techniques to prediction HR metrics such as employee retention rate, employee performance, attitude and absenteeism. The study by Nath et al (2022) developed absenteeism prediction models with the use of four ML techniques, namely, support vector machine (SVM), artificial neural network, multinomial LR and random forest (RF). The SVM-based ML model was proposed as the best model to predict absenteeism as per the experimental analysis carried out in this study.…”

Section: Machine Learningmentioning

confidence: 99%

Predicting non-violent work behaviour among employees using machine learning techniques

Lata

Garg

2023

IJCMA

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Purpose This study aims to develop a model to predict non-violent work behaviour (NVWB) among employees using machine learning techniques. Design/methodology/approach Four machine learning techniques (Naïve Bayes, decision tree, logistic regression and ensemble learning) were used to develop a prediction model for NVWB of employees. Also, 10-fold cross-validation method was used to validate the NVWB prediction models. The confusion matrix is used to derive various performance matrices to express the predictive capability of NVWB models quantitatively. Findings The model developed using random forest technique was identified as best NVWB prediction model, as it resulted in highest true positive rate and true negative rate, thereby resulting in the highest geometric mean, balance and area under receiver operator characteristics curve. Originality/value To the best of the authors’ knowledge, this is one of the pioneer studies that used machine learning techniques to develop a predictive model of NVBW.

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Section: Machine Learningmentioning

confidence: 99%

Predicting non-violent work behaviour among employees using machine learning techniques

Lata

Garg

2023

IJCMA

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Determining the temporal factors of survival associated with brain and nervous system cancer patients: A hybrid machine learning methodology

Nath

Coursey

Ekong

et al. 2023

International Journal of Healthcare Management

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Although different cancer types have been investigated from the perspective of biomedical sciences, machine learning-based studies have been scant, particularly in addressing the temporal impact of brain and central nervous system (BCNS) cancer survival. The present study aims to ll this gap by proposing a machine learning methodology to investigate the temporal effects of the attributes and the levels at which they are associated with BCNS cancer survival. Methods.Following the best practices in health analytics, the proposed methodology utilizes a variety of feature selection, data balancing, and sensitivity analysis methods to optimize the knowledge discovery process and the resultant outcomes. Results.The ndings can potentially assist medical professionals in identifying and targeting speci c subsets of features and levels of attributes associated with sharply decreasing (or increasing) survival rates; thereby implementing better treatment options to improve the survival chances of BCNS cancer patients. Conclusion.Although the proposed hybrid methodology is validated on a large and feature-rich BCNS cancer data set, it can be utilized to study survival prognostics of other cancer or chronic disease types.about 24,530 new brain and other nervous system cancer cases in the United States, and approximately 18,600 people lost their lives to the disease (National Cancer Institute, 2021).The term "BCNS cancers" is used to describe various forms of cancers or tumors that grow in the brain or the spinal cord, which are often fatal due to their invasive nature and the tendency to be resistant to typical surgical procedures and therapies (Liu & Zong, 2012). Although brain and other central nervous system cancers are rare, they exert a signi cant social and economic impact on the affected individuals, their families, and the community (Australian Institute of Health and Welfare, 2017). In addition, brain and other central nervous system cancers pose a huge burden on healthcare systems due to their inherently disabling effects on the patients (GBD 2016 Brain and Other CNS Cancer Collaborators, 2019). Unfortunately, BCNS cancers can emerge at any age. Nonetheless, according to data obtained from the United States Central Brain Tumor Registry (dataset from the National Program of Cancer Registries[NPCR] and Surveillance, Epidemiology, and End Results [SEER] registries), malignant brain tumors are most prevalent among males and non-Hispanic White individuals, while benign brain tumors are most common among females and non-Hispanic Black individuals (Liu & Zong, 2012;Ostrom et al., 2019). Analysis of the SEER program data from 1978 through 2017 reveals that the BCNS cancer incidence has increased over this period, as shown in Fig. 1 (National Cancer Institute, 2021).However, between 2008 and 2017, incidence rates for malignant BCNS cancers declined annually by 0.8% for all ages combined. Unfortunately, the rates observed among children and adolescents increased annually by 0.5-0.7% over this same period (Miller et al., 2021)...

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Artificial Neural Networks (ANNs) and Machine Learning (ML) Modeling Employee Behavior with Management Towards the Economic Advancement of Workers

Lee

2024

Sustainability

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The role of employee behavior in organizations and their interaction with management is crucial in advancing the economic progress of workers. This study examines the impact of employee behavior and management practices on organizational performance and economic progress, using advanced artificial intelligence techniques to explore complex relationships and provide evidence-based strategies for sustainable workforce development. The research analyzes critical aspects such as job satisfaction, motivation, participation, and communication to uncover the underlying mechanisms that contribute to economic development. It recognizes the dynamic relationship between employees and management, confirming the central role of effective leadership, communication, and teamwork in achieving positive results. The study emphasizes that harmonious cooperation between employees and management is necessary to create a favorable work environment that contributes to the economic development of workers. It utilizes an artificial neural network (ANN) to better understand the interdependencies between different parameters and their effects within the framework of this ongoing project. The results contribute to the existing body of knowledge by providing practical implications for organizations seeking to optimize the employee–employer relationship and increase the overall workforce productivity. By understanding the complex dynamics between employee behavior and management practices, organizations can create a supportive environment that maximizes employee potential and contributes to sustainable economic growth. The findings demonstrate an accuracy of over 70%, indicating that enhancing job satisfaction and communication can significantly improve employee participation, productivity, and overall organizational performance.

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

Cited by 3 publications

References 44 publications

Predicting non-violent work behaviour among employees using machine learning techniques

Predicting non-violent work behaviour among employees using machine learning techniques

Determining the temporal factors of survival associated with brain and nervous system cancer patients: A hybrid machine learning methodology

Artificial Neural Networks (ANNs) and Machine Learning (ML) Modeling Employee Behavior with Management Towards the Economic Advancement of Workers

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