Diabetes Care: A Machine Learning Based Review Under Supervision and without Supervision - Kuppala Saritha, D. Ganesh, P. Neelima, G. B. Hima Bindu, L.Thomas Robinson, Bingi Manorama Devi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Diabetes is a persistent metabolic condition that affects millions of people globally. The effective management of diabetes care is crucial in order to prevent complications and improve patient outcomes. Recent years have seen a substantial increase in the use of machine learning techniques in the field of healthcare, especially the treatment of diabetes. This review seeks to offer a thorough examination of machine learning techniques used in diabetes treatment, both supervised and unsupervised. Algorithms for supervised machine learning have been widely used for a variety of diabetes care activities, including risk assessment, diagnosis, and medication recommendation. These algorithms utilize labelled data to train predictive models, allowing for accurate identification of high-risk individuals, early detection of diabetes, and personalized treatment plans. In particular, support vector machines, random forests, and synthetic neural networks have produced promising outcomes in these fields of contrast, unsupervised machine learning techniques have been used for pattern identification and exploratory analysis of big datasets without specified labels. The identification of patient subgroups based on shared traits using clustering techniques like k-means and hierarchical clustering has enabled personalised therapies and precision medicine approaches in the treatment of diabetes. Principal component analysis and t-distributed stochastic neighbour embedding are two examples of dimensionality reduction techniques that have been useful in visualising complex data and revealing hidden relationships. This review also discusses the challenges and limitations associated with the application of machine learning in diabetes care. Issues such as data quality, interpretability, and generalizability of models are addressed, highlighting the importance of addressing these concerns for successful implementation in clinical practice. In conclusion, the integration of supervised and unsupervised machine learning techniques holds great potential in improving diabetes care. These methods provide valuable insights into risk assessment, diagnosis, treatment, and patient stratification. Nonetheless, further research and collaboration between data scientists, clinicians, and researchers are necessary to address the challenges and enhance the translation of machine learning algorithms into real-world clinical settings. DOI: https://doi.org/10.17762/ijisae.v12i21s.5598