The importance of innovative solutions to prevent, mitigate, or reverse prevalent common age-related health conditions has been realized. This includes immune system disorders, musculoskeletal disorders, cardiovascular diseases, neurodegenerative diseases, metabolic diseases, and even cancers. These diseases can cause death and pose a threat to public health. As multi-scale biological data continues to accumulate, many learning-based approaches have been developed and extensively used to integrate multidimensional data and study important biomedical problems. In recent years, cutting-edge technologies, including artificial intelligence, deep learning, machine learning, etc., have been implemented into study approaches, providing great progress in healthcare data-mining, especially in the field of age-related diseases.

However, there are still some scientific challenges in healthcare data-mining for age-related diseases. For instance, regarding the approaches themselves, the model accuracy is essential and how to make the models fit better is still worth considering; for age-related diseases, they are very complex and harbor the characteristics of disease heterogeneity and individualization. Whether the existing learning-based methods can best reflect the whole nature of the diseases is still suspectable; for data imperfections (deficiencies, inaccuracies, or imbalances), the learning results of the models and algorithms for healthcare data-mining will be seriously influenced.

This Special Issue focuses on learning-based approaches in healthcare data-mining for age-related diseases. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• Machine learning for healthcare big data in the field of age-related diseases
• Knowledge discovery for healthcare big data in the field of age-related diseases
• Computational biophysics for healthcare big data in the field of age-related diseases
• Big data theory and methods for computational bioinformatics in applications for healthcare big data in the field of age-related diseases
• Predictive monitoring and pattern detection in applications for healthcare big data in the field of age-related diseases
• Decision mining, recommendation, and operational support
• Data quality and management
• Prevention, diagnosis, and therapeutics
• Identification and characterization of biomarkers for age-related diseases
• Computational bioinformatics for healthcare big data in age-related diseases