A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

Tan, Joseph; Turel, Ofir; Dohan, Michael S.

doi:10.24251/hicss.2017.394

Cited by 2 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Forbes (Columbus, 2020), prevalent applications include consumer and market segmentation, computer-assisted diagnostics, call center virtual assistants, sentiment analysis/opinion mining, face detection/recognition, and human resources applications. Similarly, machine learning in the insurance industry is gaining momentum through evaluating risks, accelerating claims, and detecting fraud (Tian & Han, 2022;Hanafy & Ming, 2021;Tian, 2017). Machine learning offers more holistic, reliable, and representative data analysis and results to solve complex problems quickly.…”

Section: Machine Learning In the Fourth Industrial Revolutionmentioning

confidence: 99%

A Machine-Learning-Based Business Analytical System for Insurance Customer Relationship Management and Cross-Selling

Tian,

Todorovic,

Todorovic

2023

JABE

View full text Add to dashboard Cite

Effective cross-selling practices are integral to maintaining strong customer relationships and optimizing business processes within the insurance industry. This study presents a novel three-stage Machine Learning-Based System (MLBS) designed to enhance the identification of potential insurance customers and improve customer relationship management. This study proposes combining under sampling strategies and an ensemble approach to improve prediction performance. The proposed MLBS method involves selecting the best training sample using artificial neural networks and employing the stacking ensemble approach. It yields superior prediction results, exhibiting the highest recall, precision, and Area Under the Curve (AUC). These advancements substantially bolster the efficiency of cross-selling strategies. This research pioneers the application of stacking ensemble learning within the cross-selling domain, representing a novel contribution to the business field. The outcomes underscore the superiority of the MLBS system over baseline models across multiple performance metrics, thereby significantly enhancing support for cross-selling campaigns in various businesses.

show abstract

Section: Machine Learning In the Fourth Industrial Revolutionmentioning

confidence: 99%

A Machine-Learning-Based Business Analytical System for Insurance Customer Relationship Management and Cross-Selling

Tian,

Todorovic,

Todorovic

2023

JABE

View full text Add to dashboard Cite

show abstract

The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Samhan¹,

Crampton²,

Ruane³

2018

CAIS

View full text Add to dashboard Cite

Research has extensively demonstrated that healthcare industry has rapidly implemented and adopted information technology in recent years. Research in health information technology (HIT), which represents a major component of the Hawaii International Conference on System Sciences, demonstrates similar findings. In this paper, review the literature to better understand the work on HIT that researchers have conducted in HICSS from 2008 to 2017. In doing so, we identify themes, methods, technology types, research populations, context, and emerged research gaps from the reviewed literature. With much change and development in the HIT field and varying levels of adoption, this review uncovers, catalogs, and analyzes the research in HIT at HICSS in this ten-year period and provides future directions for research in the field.

show abstract

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

Abstract: Abstract

Cited by 2 publications

References 30 publications

A Machine-Learning-Based Business Analytical System for Insurance Customer Relationship Management and Cross-Selling

A Machine-Learning-Based Business Analytical System for Insurance Customer Relationship Management and Cross-Selling

The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Contact Info

Product

Resources

About