Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person’s growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate.

Gestational diabetes mellitus (GDM) is a syndrome that occurs among women during pregnancy and is characterized by lack of insulin hormone secretion. GDM occurs in about 4% of all pregnancies and is diagnosed at later stages of pregnancy. It can occur in women with no known history of diabetes. Since no signs or symptoms occur at the onset of GDM, it is possible to diagnose it only through screening tests. GDM poses some major health risks such as hormonal imbalance, delivery risks, and the development of Type 2 diabetes (T2D) after delivery. The condition can be diagnosed from the blood sugar level. Those diagnosed with GDM are likely to be obese, have a weak constitution, and be undergoing a stressful life or living in a stressful environment, eating unhealthy food, and living an unhealthy lifestyle. Other risk factors to be considered are family history, heredity, and the occurrence of diabetes in the past. Apart from diagnosis, the most crucial stage in managing GDM is its prognosis. If the disease is diagnosed at earlier stages, one can avoid its complications. Advanced technologies such as IoT and wearable sensors can help healthcare professionals in identifying the early signs and symptoms of GDM. In this scenario, data mining techniques are recommended for the prognosis of GDM using existing medical reports and risk factors related to women. A patient's medical history and their family history should be correlated with each other to find the likelihood of GDM occurrence. Classification is a technique in which a training dataset is used to predict the importance of related factors using an inference function. Our aim is to develop a prognosis model for GDM using a classification technique. A GDM prognosis model is developed using a training set of disease parameters along with an individual's risk factors. From the results of our experiments, it is inferred that the proposed model can be used for predicting the likelihood of GDM in its earlier stages.