Diabetes is a major health challenge in India, with cases expected to reach 124.9 million by 2045. Effective diabetes management relies on regular glucose monitoring to prevent dangerous spikes (hyperglycemia) and drops (hypoglycemia) in blood sugar levels. Managing diabetes can be difficult due to a lack of specialists, unequal access to healthcare, low medication adherence, and poor self-care. These challenges make it harder for patients to keep their blood sugar levels under control, increasing the risk of serious health problems.
New digital health technologies, especially those that use Artificial Intelligence (AI), offer a way to improve diabetes care and reduce costs. Machine learning (ML) has been used in many areas of diabetes research, from basic studies to predictive tools that can help doctors and patients make better and timely decisions. However, AI learning models, especially predictive AI models, have a few drawbacks. Many of these models work like a “black box,” meaning their predictions are difficult to understand. This lack of transparency makes it hard for doctors and patients to fully trust them. Furthermore, traditional models, such as statistical forecasting methods or basic neural networks, often fail to recognise long-term glucose fluctuations and require complex fine-tuning.
A research team at National Institute of Technology Rourkela, led by Prof. Mirza Khalid Baig, Assistant Professor, Biotechnology and Medical Engineering, has developed a new AI-driven approach to improve blood sugar predictions for people with diabetes.
The findings of this study have been published in the prestigious IEEE Journal of Biomedical and Health Informatics.
The researchers at NIT Rourkela focused on improving glucose forecasting using deep learning techniques. Their approach incorporates a specialised AI model that learns from past blood sugar trends and predicts future levels more accurately than existing methods. Unlike traditional forecasting models, which often struggle with long-term trends and require manual adjustments, this model processes glucose data automatically, identifying key patterns and making precise predictions.
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