Implementation of Artificial Neural Network to Predict Diabetes with High-Quality Health System.

Patients with diabetes who are closely monitored have a higher overall quality of life than those who are not. Costs associated with healthcare can be decreased by utilising the Internet of Things (IoT), thanks to technological advancements. To satisfy the expectations of e-health applications, it is required for the development of the intelligent systems as well as increases the number of applications that are connected to the network. As a result, in order to achieve these goals, the cellular network should be capable of supporting intelligent healthcare applications that require high energy efficiency. In this paper, we model a neural network-based ensemble voting classifier to predict accurately the diabetes in the patients via online monitoring. The study consists of Internet of Things (IoT) devices to monitor the instances of the patients. While monitoring, the data are transferred from IoT devices to smartphones and then to the cloud, where the process of classification takes place. The simulation is conducted on the collected samples using the python tool. The results of the simulation show that the proposed method achieves a higher accuracy rate, higher precision, recall, and f-measure than existing state-of-art ensemble models.

An Artificial Intelligence-Based Reactive Health Care System for Emotion Detections.

In the past few years, remote monitoring technologies have grown increasingly important in the delivery of healthcare. According to healthcare professionals, a variety of factors influence the public perception of connected healthcare systems in a variety of ways. First and foremost, wearable technology in healthcare must establish better bonds with the individuals who will be using them. The emotional reactions of patients to obtaining remote healthcare services may be of interest to healthcare practitioners if they are given the opportunity to investigate them. In this study, we develop an artificial intelligence-based classification system that aims to detect the emotions from the input data using metaheuristic feature selection and machine learning classification. The proposed model is made to undergo series of steps involving preprocessing, feature selection, and classification. The simulation is conducted to test the efficacy of the model on various features present in a dataset. The results of simulation show that the proposed model is effective enough to classify the emotions from the input dataset than other existing methods.

Hypercalcemia induced by parathyroid hormone-related peptide after treatment of carcinoma.

Squamous cell carcinoma of the head and neck is a rare cause of humoral hypercalcemia of malignancy. This paraneoplastic syndrome is usually one of the presenting symptoms of the disease. We report a case of squamous cell carcinoma of the oral cavity that presumably elaborated parathyroid hormone-related peptide (PTH-rP) and caused hypercalcemia only after radiotherapy and chemotherapy.

Pneumonia due to Mycoplasma pneumoniae with transient proteinuria.

Mycoplasma pneumoniae pneumonia can be associated with several systemic features. This report illustrates a case of M. pneumoniae pneumonia in an adult who recovered completely. Direct infection of the kidneys or an immunologic mechanism may have been the underlying cause. This patient also had simultaneous pharyngitis caused by group A beta-hemolytic streptococci. Various clinical possibilities with respect to pneumonia, pharyngitis, and proteinuria are briefly reviewed.