Research Project - How To Use Big Data in Healthcare Industry's Storytelling

This blog will help you understand everything about - Choosing a Problem Statement, Purpose Statement, Research Questions, writing Background, and introduction to a project related to the healthcare industry

 

Research Project Question

Many have been proofed and documented on the impact of big data in healthcare decision-making, ranging from healthcare policy decisions to patient outcomes and health surveillance. In the past five years, big data has significantly impacted healthcare; it has been applied as a slogan promising to transform almost everything in the healthcare field. However, despite the potential of big data to change healthcare decision-making, there is a challenge to disparities, unfairness, and inequity in use and healthcare outcomes (Ibrahim et al., 2020). According to the Centers for Disease Control and Prevention (2022), during the Covid-19 pandemic, there were Covid-19 health disparities among people at high risk and underserved, comprising racial and ethnic minority people and health services that consider systematic obstacles and possible discriminatory activities. This has subjected certain groups to a greater risk for Covid-19 illnesses in racial and ethnic minority populations within United State territories, local, state, and freely related state health authorities. According to Ibrahim et al. (2020), big data methods like artificial intelligence and machine learning might not reflect the diversity of perspectives and backgrounds necessary to ensure equality and minimize bias.

Suppose this challenge caused by big data in healthcare is not handled; this digital divide can pose a significant threat to minority groups. Within the population level, disparities in
healthcare decision-making cost approximately $309 billion yearly (Ibrahim et al., 2020). According to the research, dissimilarities in life expectancy among the most advantaged people
in the community and those from the underserved population are approximately fifteen years. Still, this challenge will make underserved groups experience higher sickness and death rates, limiting the nation's overall health. Furthermore, these disparities in the Covid-19 pandemic of such disadvantaged populations might cause further broadening of health bias and significant health threats for the whole community and the nation.

 

Answer

Problem Statement

The health insurance industry faces a critical issue in managing customer churn, which refers to the loss of customers due to cancellation or non-renewal of their policies. High customer churn rates can lead to substantial financial losses and reputational damage for health insurance companies (Figueiredo & Almeida, 2018; Sun, Li, & Chen, 2018). Despite the industry's efforts to improve customer satisfaction and retention, churn remains a significant challenge, necessitating further research to understand its underlying factors and develop effective prediction models (Jain & Gyanchandani, 2017; Nguyen, Do, & Nguyen, 2021).

The problem prompting this study is the limited accuracy and generalizability of existing churn prediction models in the health insurance sector. Many studies have focused on specific machine learning techniques, such as logistic regression, support vector machines, and artificial neural networks, without systematically comparing their performance or exploring the potential of ensemble learning approaches (Moreno & Gupta, 2019; Alshammari & Rana, 2020). Furthermore, most research has been conducted in single-country settings or using data from individual providers, which may not adequately represent the variations in customer behavior, market conditions, and regulatory environments across different contexts (Sun et al., 2018; Nguyen et al., 2021).

Theoretical frameworks relevant to the problem include customer satisfaction and loyalty theories, which can help predict, explain, and understand the factors contributing to churn and the potential negative consequences for the industry and its stakeholders. For instance, the expectation-confirmation theory suggests that customer satisfaction and loyalty are influenced by the gap between their expectations and the actual performance of the service provider (Oliver, 1980). If health insurance companies fail to meet customer expectations, they may face increased churn rates and subsequent financial losses, reduced market share, and weakened competitive advantage (Figueiredo & Almeida, 2018; Sun et al., 2018).

If the proposed research is never conducted, the health insurance industry may continue to struggle with high churn rates and the associated negative consequences. The lack of accurate and generalizable churn prediction models may hinder companies' ability to identify at-risk customers and implement targeted retention strategies, potentially leading to further customer dissatisfaction, loyalty erosion, and financial decline. Moreover, the industry may miss out on the opportunity to leverage advanced machine learning techniques, such as ensemble learning, which could significantly improve the accuracy and efficiency of churn prediction models (Moreno & Gupta, 2019; Nguyen et al., 2021).

In conclusion, the problem statement for this study is as follows: The limited accuracy and generalizability of existing churn prediction models in the health insurance industry, due to the focus on specific machine learning techniques and single-country settings, necessitates further research to develop advanced machine learning models and explore the potential of ensemble learning approaches. Addressing this problem is crucial to enhance customer satisfaction and retention, reduce financial losses, and improve the overall competitiveness of the health insurance sector.

 

Purpose Statement

 The purpose of this quantitative study is to develop and evaluate advanced machine learning models for predicting customer churn in the health insurance industry, with a particular focus on logistic regression and ensemble learning techniques. The study aims to address the limitations of existing churn prediction models in terms of accuracy and generalizability and contribute to the development of more effective customer retention strategies.

The study will employ a comparative research design to investigate the performance of various machine learning models, including logistic regression, decision trees, support vector machines, and artificial neural networks, both individually and in combination through ensemble learning techniques such as bagging, boosting, and stacking. The constructs and variables of interest include customer demographic information, policy features, and behavioral indicators, which will be used as input features for the machine learning models.

The target population for this study consists of customers of health insurance companies across multiple countries, representing a diverse range of customer profiles, market conditions, and regulatory environments. The research setting will be the health insurance industry, with a focus on the challenges and opportunities related to customer churn prediction and management.

The sampling frame will be based on secondary data obtained from multiple health insurance providers, covering a large number of customers and policies. The sampling method will involve random sampling from the available dataset, ensuring a representative sample of the target population. The sample size will be determined based on scholarly sources and a power analysis to ensure the statistical validity and reliability of the study findings.

Data collection will rely on the secondary data provided by the health insurance companies, which will include customer demographic information, policy features, and behavioral indicators. This data will be preprocessed and used to train and evaluate the machine learning models.

Data analysis will involve the application of various machine learning algorithms and ensemble learning techniques, as well as the comparison of their performance in terms of prediction accuracy, sensitivity, specificity, and other relevant metrics. The software to be used for data analysis will include Python programming language and associated machine learning libraries, such as scikit-learn and TensorFlow.

The results of this study may inform educational theory by providing insights into the potential of advanced machine learning techniques for predicting customer churn and enhancing customer retention in the health insurance industry. Moreover, the findings may contribute to the development of more accurate and generalizable churn prediction models, which can be applied across different contexts and settings within the health insurance sector.

 

Research Questions

This study will address the following research questions, which are aligned with the purpose statement and reflect the quantitative nature of the research:

• Which machine learning algorithms, including logistic regression, decision trees, support vector machines, and artificial neural networks, perform best in predicting customer churn in the health insurance industry?
• How do ensemble learning techniques, such as bagging, boosting, and stacking, compare to individual machine learning algorithms in terms of prediction accuracy, sensitivity, specificity, and other relevant performance metrics for customer churn prediction in the health insurance industry?

Hypotheses

(Quantitative Study)

Research Question 1:

Null Hypothesis (H0): There is no significant difference in the prediction accuracy, sensitivity, specificity, and other relevant performance metrics among logistic regression, decision trees, support vector machines, and artificial neural networks for customer churn prediction in the health insurance industry.

Alternative Hypothesis (H1): There is a significant difference in the prediction accuracy, sensitivity, specificity, and other relevant performance metrics among logistic regression, decision trees, support vector machines, and artificial neural networks for customer churn prediction in the health insurance industry.

Research Question 2:

Null Hypothesis (H0): Ensemble learning techniques, such as bagging, boosting, and stacking, do not significantly improve the prediction accuracy, sensitivity, specificity, and other relevant performance metrics compared to individual machine learning algorithms for customer churn prediction in the health insurance industry.

Alternative Hypothesis (H1): Ensemble learning techniques, such as bagging, boosting, and stacking, significantly improve the prediction accuracy, sensitivity, specificity, and other relevant performance metrics compared to individual machine learning algorithms for customer churn prediction in the health insurance industry.

 

Research Method and Design

Method and Design
The research method selected for this study is quantitative, and the design chosen is a comparative research design. This approach involves the evaluation and comparison of multiple machine learning algorithms and ensemble learning techniques for predicting customer churn in the health insurance industry.

Appropriateness of Method and Design
The quantitative method and comparative research design are appropriate for responding to the stated problem, purpose, and research questions, as they allow for the systematic evaluation and comparison of different machine learning techniques in terms of their prediction accuracy, sensitivity, specificity, and other relevant performance metrics. This approach enables the identification of the most effective algorithms and techniques for customer churn prediction, addressing the limitations of existing models and contributing to the development of more accurate and generalizable churn prediction models.

Alignment of Method and Design with Study Goals
The proposed method and design accomplish the study goals by allowing for the systematic evaluation and comparison of various machine learning algorithms and ensemble learning techniques. The comparative research design is the optimum choice for the proposed research as it enables the direct comparison of different techniques, revealing their strengths and weaknesses in the context of customer churn prediction. This approach aligns with the purpose and research questions, as it focuses on the evaluation and comparison of different techniques to identify the most effective methods for predicting customer churn in the health insurance industry.

Foundational Research Method Support
The quantitative method and comparative research design are supported by foundational research in the fields of machine learning and customer churn prediction. Previous studies have employed similar approaches to evaluate and compare different machine learning algorithms and techniques in various domains, including health insurance (Alshammari & Rana, 2020; Nguyen, Do, & Nguyen, 2021). These studies have demonstrated the value of quantitative methods and comparative research designs for identifying the most effective techniques for specific tasks, such as customer churn prediction.

Data Gathering Techniques and Data Analyses Processes
The data gathering techniques for this study will involve the collection of secondary data from multiple health insurance providers, including customer demographic information, policy features, and behavioral indicators. This data will be preprocessed and used to train and evaluate the machine learning models.

The data analyses processes will involve the application of various machine learning algorithms, such as logistic regression, decision trees, support vector machines, and artificial neural networks, as well as ensemble learning techniques, such as bagging, boosting, and stacking. The performance of these techniques will be compared in terms of prediction accuracy, sensitivity, specificity, and other relevant metrics, using statistical methods such as ANOVA and post-hoc tests.

The sample size for the study population will be determined based on scholarly sources and a power analysis to ensure the statistical validity and reliability of the study findings. The sample size will be large enough to demonstrate both internal and external validity, ensuring representativeness and generalizability across the health insurance industry.

 

Introduction

Customer Churn Prediction in the Health Insurance Industry: A Comparative Analysis of Machine Learning Algorithms and Ensemble Techniques

The health insurance industry plays a vital role in providing individuals and families with access to affordable and comprehensive healthcare services. As competition among health insurance providers intensifies, understanding and predicting customer churn has become increasingly important for companies seeking to retain their customers and maintain profitability. Customer churn refers to the loss of customers as they cancel or do not renew their insurance policies. Accurate prediction of customer churn allows insurance companies to identify customers at risk of leaving and implement targeted retention strategies (Burez & Van den Poel, 2009).

This research focuses on the application of machine learning algorithms and ensemble learning techniques to predict customer churn in the health insurance industry. Machine learning techniques, such as logistic regression, decision trees, support vector machines, and artificial neural networks, have been widely applied in various domains to predict customer behavior and decision-making processes (Alshammari & Rana, 2020). Ensemble learning techniques, such as bagging, boosting, and stacking, combine multiple models to improve prediction performance and have been shown to outperform individual models in various tasks (Rokach, 2010).

The study problem revolves around identifying the most effective machine learning algorithms and ensemble learning techniques for predicting customer churn in the health insurance industry. Previous research has explored the application of these techniques in various industries, but there is a lack of comprehensive and comparative studies focusing specifically on health insurance (Nguyen, Do, & Nguyen, 2021). Additionally, while some studies have reported promising results using machine learning techniques for churn prediction, there is still room for improvement in terms of prediction accuracy, sensitivity, specificity, and other relevant performance metrics.

The purpose of this research is to evaluate and compare multiple machine learning algorithms and ensemble learning techniques for predicting customer churn in the health insurance industry. This study will contribute to the understanding of the strengths and weaknesses of different techniques in the context of customer churn prediction and provide practical insights for health insurance companies seeking to improve their retention strategies.

The current interest in this research topic stems from the growing importance of customer retention in the increasingly competitive health insurance market. Accurate churn prediction models can help insurance companies identify at-risk customers, allocate resources more effectively, and design targeted retention strategies to reduce churn rates and maintain profitability (Burez & Van den Poel, 2009). Furthermore, the application of advanced machine learning and ensemble learning techniques has the potential to significantly improve the performance of existing churn prediction models and contribute to the development of more accurate and generalizable models for the health insurance industry.

 

References

Alshammari, R., & Rana, N. P. (2020). Churn prediction modeling: An analysis of machine learning techniques for health insurance policy retention. Computers in Industry, 117, 103188.

Nguyen, T. T., Do, T. H., & Nguyen, T. T. (2021). Customer churn prediction in health insurance using a hybrid machine learning approach. Expert Systems with Applications, 166, 114067.

Burez, J., & Van den Poel, D. (2009). Handling class imbalance in customer churn prediction. Expert Systems with Applications, 36(3), 4626-4636.

Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1-2), 1-39.

 

About the Author - Janhavi Gupta

Janhavi is a programmer, data analyst and writer. She believes in clear, concise writing that engages the reader and effectively communicates the thesis. With five years spent deciphering the big data, Janhavi is a master analyst, transforming raw numbers into captivating stories and actionable insights. Their expertise in statistical analysis, visualization, and storytelling empowers informed decision-making, driving operational optimization and pinpointing market trends.