Sistem kebal buatan terhadap pemantuan ketidaknormalan pesakit dalam waktu nyata

Abstract

In a hospital or clinic many patients need care and monitoring, especially patients in Intensive Care Unit (ICU). These services can be integrated with technology that offers online and real-time monitoring. Many researches related to patient detection and monitoring have been done but only a few studies have highlighted data analysis and processing of anomalies of patient behavior. Detection of anomalies data is important as this would serve as an alert or warning for the hospital to take the necessary actions. Therefore, this research explored data analysis and processing of anomalies using Artificial Immune System (AIS) which would be applicable for future patients. AIS is an intelligent computational technique based on the human immunology system and used in many areas such as computer systems, pattern recognitions and stock market trading. In AIS, Real Valued Negative Selection Algorithm (RNSA) is used for detecting anomalies of a patient’s body parameters such as temperature, blood pressure and body mass index. In the algorithm, a patient’s data is obtained from the monitoring system or database and classified as a real value. The value is compared with the distance of data, where the minimum distance is set to 0.05 which is based on the raw data received from the system. If the distance is less than the Negative Selection Algorithm (NSA) detector distance, then the data will be classified as abnormal. In this research, AIS developed as a real time detection and monitoring system was connected to Radio Frequency Identification (RFID) technology. The results showed that the RNSA with the active RFID tag attached with a temperature sensor is able to detect the patient’s body temperature and send the signal to the backend used wireless system. The proposed systems and designs have contributed to healthcare management as the technology serves as an early warning detector of anomalies in patients.