Smart Health Monitoring System – A Comparative Study

Abstract

-Coronary heart disease is the prime cause of death worldwide. In the field of human work, there is a lot of information available, and specialized techniques are used to process this information. One of the methods that is frequently used is handling or processing. The strategy anticipates a range of future events and cardiovascular issues. A previous cardiovascular disease prediction was made using this technique. For manageable IoT, a pulse sensor for observing pulses is used. Sensor readings are transferred into CSV-looking data via IFTTT, which are viewed on Google Sheets. To prepare, and test the treated datasets, facts were used to organize the datasets. The method uses a mechanism for information preparation to evaluate these criteria in stages. Artificial intelligence is used for computation and categorizing tasks. The dataset is initially dissected, examined, and filtered. The input is then analysed using Python programming and machine learning techniques, including KNN algorithms and XG boost outlying classifier approaches. In terms of accuracy, MLP (Multilayer promotion) showed higher results for detecting heart disease. The proposed system then showed its capability to predict past heart issues with high accuracy. Thanks to the suggested hardware and software solution, patients can anticipate cardiac sickness before it becomes serious enough. Because of this, mass screening programs will be more practical in areas lacking hospitals (i.e., rural areas).