Abstract

Remote health care is needed when direct medical monitoring is unavailable. Modern technology provides several ways to make patient access simpler. In particular, cloud, IoT, and data mining technologies have been successful in health care and medicine. This method identifies patient illnesses using data collected from two groups of patients, a male group and a female group where data are based on the heart rate, systolic blood pressure, and body temperature parameters. The collected data is used by the three types of classifiers which are the naïve Bayes, support vector machine (SVM), and the J48. These three classification methods were considered to measure the classification accuracy using the proposed healthcare IOT-based monitoring system. The Precision, Recall, and F-measure evaluation measurements were used to quantify the obtained accuracy results of the two groups. The obtained results showed excellent classification results that were 0.96, 0.99, and 0.98 for the naïve Bayes classifier to the male group that outperformed the results of the other two classifiers for the precision, recall, and f-measure, respectively. As for the female group, similar results have been obtained. In conclusion, the suggested IoT-based monitoring system is an easy-to-use and accurate tool that can be used to monitor patients remotely.