Health status classification model for medical adherence system in retirement township

Medical adherence and remote patient monitoring have gained huge attention from researchers recently, especially with the need to observe the patients’ health outside hospitals due to the ongoing pandemic. The main goal of this research work is to propose a health status classification model that provides a numerical indicator of the overall health condition of a patient via four major vital signs, which are body temperature, blood pressure, blood oxygen saturation level, and heart rate. A dataset has been prepared based on the data obtained from hospital records, with these four vital signs extracted for each patient. This dataset provides a label associating each patient to the number of medical diagnoses. Generally, the number of diagnoses correlates with the patient's medical condition, with no diagnoses indicating normal condition, one to two diagnoses suggest low risk, and more than that implies high risk. Thus, we propose a method to classify a patient’s health status into three classes, which are normal, low risk and high risk. This would provide guidance for healthcare workers on the patient's medical condition. By training the classification model using the prepared dataset, the seriousness of a patient's health condition can be predicted. This prediction is performed by classifying the patients based on their four vital signs. Our tests have yielded encouraging results using precision and recall as the evaluation metrics. The key outcome of this work is a trained classification model that quantifies a patient's health condition based on four vital signs. Nevertheless, the model can be further improved by considering more input features such as medical history. The results obtained from this research can assist medical personnel by providing a secondary advice regarding the health status for the patients who are located remotely from the medical facilities.