Abstract

Clinical Decision Support Systems (CDSS) Fuzzy model helps medical diagnosis, monitoring and classifications of the vital signs of Intensive Care Unit (ICU) patients which provide support for decision-making in patient care. A category of such patients is the prematurely born babies, which are placed in infant incubators of Neonatal Intensive Care Unit (NICU) for continuous monitoring of their body vital signs (temperature, heart rate and respiration). However, the development of Fuzzy rule based CDSS for classification of neonates’ health status is limited and still manually monitored in many developing countries like Nigeria. This work developed Fuzzy Inference System-CDSS rules that can be used to efficiently classify the neonate’s condition in the incubators of NICU. A Fuzzy Inference System CDSS (FIS-CDSS) was developed for the three inputs: Temperature, Heart rate and Respiration rate (THR) based on their membership functions’ value (low, medium, high) and twenty-seven (27) IF-THEN fuzzy rules using fuzzy logic toolbox in Matrix Laboratory 8.1 (R2013a). The FIS-CDSS maps the THR to an output status (Normal, Abnormal and Critical). The vital signs’ readings were fed into the FIS-CDSS, which fuzzifies them and classifies the health status of the neonates. This work developed a Fuzzy-rule based system that can efficiently classify neonates’ health status which provides adequate and accurate information for on-the-spot assessment of neonates for decision making that improves the mortality rate and recovery period of neonates. Also, the system could provide baseline information for other researchers in developing fuzzy rule based CDSS for other categories of patients.