Karpagam Hospital

The specific form of statistical quality control, applied to the medical field to ensure product excellence and patient safety. Specifically, the proposed method employs a Machine Learning Algorithm (MLA) to assist in decision-making about accepting or rejecting inspected medical samples. The algorithm is trained using data from double sampling plan tables, enabling it to generate closed-form solutions for sample size while accounting for producer and consumer risks. This automation ensures precision in interpreting double sampling plan tables without compromising quality. provides flexibility for medical quality controllers, allowing for the consideration of specific requirements while minimizing time and cost during inspections. It addresses producer and consumer risks at predefined levels, while also incorporating other key quality parameters. With this system, medical professionals can estimate sample size at fixed producer and consumer risk levels, or predict these risks at a given sample size, ensuring comprehensive risk management and quality assurance in medical products.