Privacy and Security of Bio-inspired Computing of Diabetic Retinopathy Detection Using Machine Learning

The diagnosis and detection of numerous diseases has advanced significantly in the healthcare sector, which is always changing. One illness that has significantly impacted humankind is diabetes, a condition that directly impacts blood glucose levels. Glucose, or sugar, is the primary source of energy for our bodies, and it is derived from the food we consume. Insulin, produced by the pancreas, assists glucose in entering the cells of the body. But diabetics are either unable to use their own insulin well or do not create enough of it, resulting in increased levels of carbohydrates in the body. New diseases are being diagnosed at an alarming rate, which is indicative of the impact that changes in our lifestyle habits have had on our health. This paper is about fulfilling two major objectives, i.e. (i) The dataset has been made secured by applying encryption generation key to it. This will help in maintaining the privacy of the patients and also will avoid unauthorized access. (ii) Secondly, in order to predict diabetic retinopathy in the patient’s various machine learning models have been used. This work truly shows the importance of data security and data preservation using cryptography (Fernet). It can help clinicians in making better decisions during critical stages of treatment. Our findings show how well machine learning and data security operate to diagnose diabetic retinopathy, and they also point to areas that could be improved in the future with the use of deep learning models and frameworks.