Prediction of Heart Disease and Chronic Kidney Disease Based on Internet of Things Using RNN Algorithm

Nowadays, people are very serious and aware about their health condition, even though people are running towards their work and busy time schedule, they are not taking care of themselves until it shows any kind of symptoms. Weight gain and obesity are the two issues affecting productivity and quality of life throughout the globe. The internet of things (IoT) plays an important role in achieving the shared goal by linking, detecting, recognising and processing data between devices or services. IoT in healthcare provides us the advantages of monitoring, analysing, diagnosing and controlling of the different conditions of overweight and obesity. Also provides solution for the prevention of weight gain and obesity. Since IoT has a limited resources of objects used in it, another alternative method is being introduced for the above mentioned advantages such as machine learning. People who have a high risk of cardiovascular disease may also have a chance of getting kidney diseases, and it can be treated accordingly with the help of historical medical records. But chronic kidney disease (CKD) is an illness that shows no diagnostic signs at all and it is difficult to identify, detect and prevent CKDs and it may sometimes permanently harm the health system, so that the prediction and analysis of therapy are done in machine learning. The main aim of this study is to establish a predictive model for the CKD heart disease data to analyse different open-source Python module and to achieve outcomes, and the 96% prediction and precision machine learning methods may be established by the comparison with various algorithms such as K-nearest neighbours (KNNs) and recurrent neural network (RNN). A data set which is gathered from patient’s medical history is predicted by using this algorithm. Based on the amount of potassium in patient’s blood, the predicted value provides us the clarification that the person will get chronic kidney disease or not.