Data Collection for Classification in IOT and Heart Disease Detection

Abstract

Health predictions are a key technology-focused field. The software plays an important role in reliably and at a good rate predicting diseases. This work focuses on the study of methods that are used for purposes of prediction. The disease detection parameters are obtained through the use of sensors and maintained in the form of datasets. The Internet of Things(IoT) is used mainly to collect user data. For evaluation, techniques like Euclidean distance, K nearest neighbour, and ARIMA are considered. This work also highlights relative strengths and weaknesses. Data reliability may be at risk as the sensor may not function during the collection process. Various algorithms also possess fault tolerant capabilities.