Internet of Things based Type 2 Diabetes Prediction using Enhanced Feed Forward Neural Network with Particle Swarm Optimization
Keywords:
Internet of Things, Type 2 diabetes, Enhanced feed forwarded Neural Network, Chaotic-based particle swarm optimization modelAbstract
The Internet of Things (IoT) is an emerging network that enables everyday objects to connect to the web and exchange and collect data. The IoT is crucial in healthcare because it allows for constant patient monitoring and informed decision making. Diabetic complications now impact a sizable fraction of the population. The elderly are disproportionately affected by type 2 diabetes, which is also the most prevalent form of the illness and which is associated with a wide range of serious health issues such as cardiovascular disease, renal failure, blindness, stroke, and even death. That's why knowing the patient's prognosis or receiving a diagnosis quickly may help. Improving the prediction model's accuracy takes time and work, but one of the biggest challenges is figuring out how to properly analyze the data to get the right conclusion. Many models may be employed for analysis; for instance, many Neural Network models have been used for clinical diagnosis. The problem is that these models haven't improved much, in terms of either accuracy or precision, whether in the training or testing stages of sickness diagnosis. This study offers an Enhanced Feed forwarded Neural Network (EFNN) that employs a chaotic-based particle swarm optimization model (EFNNCPSO) to analyze IoT-based datasets. The proposed method has the potential to improve the accuracy of predicting Type 2 diabetes in an IoT environment. The suggested network is able to learn all of the features in the dataset and performs efficient calculations. Finally, analogous models to the one proposed are compared. The proposed EFNNCPSO has a higher accuracy than state-of-the-art methods (99.9%).
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