Finding Significance of HbA1c Test in Diabetes Management using Machine Learning

Diabetes is a chronic disease affecting the huge number of people worldwide with negative side effects including neuropathy, nephropathy, heart-attack, retinal degeneration etc. Effective management is necessary to mitigate these side effects. In many cases when FBS and PPS tests are performed on individuals the report seems to be in the normal range but when an HbAlc test is performed on them the report shows that they are diabetic or their glucose level is not under control. Recognizing this context, the study explores the significance of HbAlc test through machine learning techniques. In this work, two types of datasets are used. One dataset incorporates HbA1c and another dataset is without HbAlc. Machine learning (ML) algorithms including long short-term memory (LSTM), support vector mechanism (SVM) and K-nearest neighbor (KNN) has been employed to show that the HbA1c test helps better detection in diabetes and management. This approach will provide healthcare workers a better insight of patients and can attain glucose within the targeted range for long-term care.