An Innovative Internet Of Things (IoT) Computing-Based Health Monitoring System With the Aid of Machine Learning Approach
Bhupendra Kumar, Namita Rajput, Nagesh Yagnam · 2022
The community health area comprises a massive amount of information, and particular approaches are used to handle that information. One of the most common approaches is handling as well as processing. This technique forecasts the likely consequences of cardiovascular disease. The outcome of this method is to predict the preceding heart illness. The job controls IOT using a sensor (a pulse sensor to monitor pulses) and Arduino, and the results may be seen on a sequential screen. IFTTT is used to analyze sensor readings in Google Sheets, which are subsequently converted into CSV go-like data. The datasets used are classified according to treatment parameters, in addition to being used for data preparation and testing. This technique evaluates those parameters using the information preparation order method. With AI computations and categorization work. The dataset is first dissected, examined, and screened, after which the gathered data is processed in Python programming using Machine Learning Algorithms, namely Decision Tree Algorithm with Random Backwoods Classification Algorithm. SVM (Support vector machine) produces the best results in terms of detecting heart disease. As a result, the suggested paradigm is shown to be a reliable one for predicting past heart disease. The suggested hardware and software technology assists patients in predicting cardiac disease in its initial stages.