Survey on Non-Invasive Glucose Monitoring and Glycemia Detection Using Machine Learning and Signal Analysis Techniques

Diabetes or Hyperglycemia, a condition where an individual is characterized by significantly elevated blood sugar levels, may pose a significant threat to the effective lifespan as well as may pose a significant risk for various cardiovascular diseases. Reliable and non-invasive monitoring of hyperglycemia and also hypoglycemia is important for timely intervention and prognosis. The paper presents an extensive and structured survey dealing with the non-invasive glucose monitoring and diabetes detection using machine learning and signal analysis techniques. The paper focuses on a comparative analysis approach which showcases the literature in tabular and diagrammatic form. Examination of 10 papers that deal with Photoplethysmography (PPG) and Electrocardiography (ECG) signals to detect glucose variations using machine learning techniques has been carried out. The review highlights the respective proposed system, unique findings, improvements, techniques, methods, future prospects, comparison with previous studies, feature importance, and model evaluation as well as stated accuracy. This comprehensive analysis aims to provide insights into the methodologies in non-invasive glycemic conditions thereby contributing to the development of improved disease analysis.