Improved fuzzy control charts for monitoring defined health ranges using trapezoidal fuzzy numbers

Abstract

Healthcare monitoring requires precise and efficient methods to monitor individual health measurements, particularly for diseases with well-defined clinical ranges. Traditional control charts struggle to handle uncertainty in medical data, necessitating more flexible approaches. This study introduces two novel fuzzy control charts: the fuzzy moving average control chart (FMACC) and the fuzzy weighted moving average control chart (FWMACC), which utilize trapezoidal fuzzy numbers (TrFNs) to enhance monitoring capabilities. An α-cut midrange approach is applied to better capture variability, and fuzzy process capability indices (FPCIs) are incorporated to assess process performance under uncertain conditions. The proposed method is applied to creatinine and PCR data, demonstrating its versatility in health monitoring. Monte Carlo simulations validate the effectiveness of FMACC and FWMACC, confirming their superior performance in detecting small process shifts. The findings highlight the effectiveness of proposed control charts for healthcare applications, offering a significant advancement in statistical process monitoring by integrating fuzzy logic. This approach provides a robust tool for healthcare professionals to monitor patient data more reliably and efficiently.