An Optimization-Based Diabetes Prediction Model Using CNN and Bi-Directional LSTM in Real-Time Environment

Featured Application Diabetes is a common chronic disorder defined by excessive glucose levels in the blood. A good diagnosis of diabetes may make a person's life better; otherwise, it can cause kidney failure, major heart damage, and damage to the blood vessels and nerves. As a result, diabetes classification and diagnosis are vital tasks. By using our proposed methodology, clinicians may obtain complete information about their patients using real-time monitoring. To gain new insights, they can combine historical information with current data, making it easier for them to perform more thorough and comprehensive treatments than before, and they will be able to provide proactive care, which will help to improve health outcomes and reduce hospital re-admissions. Diabetes is a long-term illness caused by the inefficient use of insulin generated by the pancreas. If diabetes is detected at an early stage, patients can live their lives healthier. Unlike previously used analytical approaches, deep learning does not need feature extraction. In order to support this viewpoint, we developed a real-time monitoring hybrid deep learning-based model to detect and predict Type 2 diabetes mellitus using the publicly available PIMA Indian diabetes database. This study contributes in four ways. First, we perform a comparative study of different deep learning models. Based on experimental findings, we next suggested merging two models, CNN-Bi-LSTM, to detect (and predict) Type 2 diabetes. These findings demonstrate that CNN-Bi-LSTM surpasses the other deep learning methods in terms of accuracy (98%), sensitivity (97%), and specificity (98%), and it is 1.1% better compared to other existing state-of-the-art algorithms. Hence, our proposed model helps clinicians obtain complete information about their patients using real-time monitoring and can check real-time statistics about their vitals.