Random Forest and LightGBM-Based Human Health Check for Medical Device Fault Detection

Abstract

Medical devices are items used directly or indirectly in the human body and are a prerequisite for hospital treatment of patients, and their quality can have a direct impact on the health of patients, so strengthening the quality control of medical device use is a hot spot of concern in the clinic. Current medical device testing can reduce the occurrence of adverse events, but it cannot be completely avoided, and its work still needs to be further strengthened. In this paper, we design a two-way feature selection algorithm based on PSO_RF. We use random forest to calculate the importance of the feature attributes of the sample data and sort the results in descending order, where a particle swarm algorithm is introduced to optimize the parameters of the random forest algorithm. The 245 medical device adverse event reports received by the testing center were selected, the occurrence and types of adverse events were analyzed retrospectively, and quality control countermeasures for medical device use were formulated.

Machine-Learning-Based Detection of Pressure-Induced Faults in Continuous Glucose Monitors

“…Three broad categories include quantitative and qualitative model-based methods and retrospective history-based methods . Fault detection has been applied to healthcare, , with many machine learning − and deep learning approaches seeing success. However, model-based or rule-based approaches are primarily used for CGM fault detection.…”

Retracted: Random Forest and LightGBM‐Based Human Health Check for Medical Device Fault Detection

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”