Abstract:

Objective: This study aims to provide an overview of the current status and emerging trends in machine learning (ML) applications for chronic disease management? analyze ...Show More

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Abstract:

Objective: This study aims to provide an overview of the current status and emerging trends in machine learning (ML) applications for chronic disease management? analyze the research hotspots and frontiers in this field, and provide a reference for further development and in-depth research of ML in chronic disease management in the future. Methods: The methodology involved searching the Web of Science core database for relevant literature on ML and chronic disease management up to June 23, 2024. Visualization and analysis were performed with the help of CiteSpaces (6.3.R1), VOSviewer (1.6.20), and R (4.4.1) software. Results: A total of 1070 papers were included, and the number of ML research publications in chronic disease management showed an exponential increase in general. Deep learning is the most popular machine learning algorithm, and prediction is the hottest machine learning method. Hotspot diseases are diabetes and its complications, cardiovascular diseases, and popular applications are risk prediction, diagnosis, and individualized treatment. Conclusion: The growth of ML in the area of chronic diseases is penetrating deeper into all aspects of management. Future research can enhance ML’s role in chronic disease management by continuously optimizing algorithms and integrating multi-source data.

Date of Conference: 20-22 December 2024

Date Added to IEEE Xplore: 17 April 2025

Conference Location: Hangzhou, China

Funding Agency:

I. Introduction

Chronic diseases have emerged as a significant public health concern, posing a threat to patients’ well-being due to their prolonged duration, propensity for recurrence, and low rates of cure[1]. The World Health Organization reports that chronic diseases are responsible for 41 million deaths annually, comprising 74.0% of global mortality[2]. Managing chronic diseases necessitates sustained medical interventions and patient self-care, placing considerable strain on healthcare resources and impacting patients’ quality of life[3]. In recent years, Artificial Intelligence (AI) technologies have made significant progress in various fields, especially in healthcare. Machine learning(ML), a branch of AI, enables computer systems to learn from data and independently make decisions and forecasts based on acquired knowledge and experience [4]. The evolution of ML technology has opened up new possibilities for chronic disease management, enhancing healthcare service efficiency and offering renewed hope to patients through precise prediction and personalized treatment. Currently, while many studies explore the application of machine learning in the field of chronic disease, there is a lack of bibliometric analysis to systematically summarize and evaluate the scale and quality of existing research. Therefore, this study employs bibliometric techniques to summarize and examine the prevalent research themes and trends in ML applications for chronic disease management globally, utilizing high-frequency keyword analysis, co-occurrence network mapping, and clustering analysis to inform future research endeavors.

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