Integration of Predictive Techniques in Diabetic Retinopathy Using Deep Learning

– Diabetes is a metabolic disorder that is affecting millions of people worldwide, with its incidence rates increasing alarmingly year after year. Diabetes complications in most of the body’s major organs can be life-threatening unless treated. Early detection of diabetes is crucial for timely treatment to prevent the disease from developing complications. RR-interval signals, in the form of heart rate variability (HRV) signals, can be effectively used for non-invasive diabetes screening. This research paper proposes a classification technique for diabetic and normal HRV signals using deep learning models. We apply long short-term memory (LSTM), convolutional neural network (CNN), and their combinations (CNN-LSTM) to extract complex temporal dynamic features of the input HRV signals. These features are then classified with the assistance of support vector machine (SVM). We have achieved performance improvements of 0.03% and 0.06% in CNN and CNN-LSTM architectures, respectively, compared to our previous research work that did not use SVM. Various machine learning techniques are employed to carry out predictive analytics over big data in multiple domains. Predictive analytics in healthcare is a challenging task, but ultimately, it can help practitioners make big data-informed, timely decisions regarding patients’ health and treatment. This study explores the application of predictive analytics in the healthcare sector by utilizing six distinct machine learning algorithms. For experimentation, a dataset of patient medical records is used, and six different machine learning algorithms are applied. In this project, we use the Convolutional Neural Network (CNN) algorithm. A discussion and comparison of the performance and accuracy of the algorithms used shows which method is most suitable for diabetes prediction. This project aims to assist doctors and practitioners in the early detection of diabetes using deep learning methods.