Published online Dec 24, 2022. doi: 10.5306/wjco.v13.i12.967
Peer-review started: August 1, 2022
First decision: November 11, 2022
Revised: November 17, 2022
Accepted: December 8, 2022
Article in press: December 8, 2022
Published online: December 24, 2022
Nowadays, predictive models based on advanced algorithms have been gradually applied to the medical field, which also enables many diseases to be detected and diagnosed early. Among them, the machine learning (ML) algorithm relies on repeated iterative operations to accurately output the results. Therefore, it can improve the accuracy and robustness of prediction.
Given the superior ability of the ML-based algorithm to improve the accuracy of muscular invasion prediction, we applied the ML-assisted decision-support model to assess the risk of urinary tract infection (UTI) using clinical parameters and direct clinical decision-making prior to treatment decisions.
We developed an ML assistant model for the prevention and control of nosocomial infection.
A total of 674 elderly patients with ovarian cancer treated between January 31, 2016 and January 31, 2022 and met the inclusion criteria of the study were selected as the research subjects. A retrospective analysis of the postoperative UTI and related factors was performed by reviewing the medical records. Five ML-assisted models were developed using two-step estimation methods from the candidate predictive variables. The robustness and clinical applicability of each model were assessed using the receiver operating characteristic curve, decision curve analysis and clinical impact curve.
A total of 12 candidate variables were eventually included in the UTI prediction model. Models constructed using the random forest classifier (RFC), support vector machine, extreme gradient boosting, artificial neural network and decision tree had areas under the receiver operating characteristic curve ranging from 0.776 to 0.925. The RFC model, which incorporated factors such as age, body mass index, catheter, catheter intubation times, blood loss, diabetes and hypoproteinaemia, had the highest predictive accuracy.
These findings demonstrated that the ML-based prediction model developed using the RFC can be used to identify elderly patients with ovarian cancer who may have postoperative UTI. This can help with treatment decisions and enhance clinical outcomes.
Using an ML-based algorithm, we developed a feasible and robust method to identify factors that are significant for predicting UTIs. The RFC, which can improve the prediction and early detection of UTIs in patients with ovarian cancer, was particularly robust. In addition, the five most crucial factors were age, body mass index, catheter, catheter intubation times, blood loss, diabetes and hypoproteinaemia. Clinicians may find it extremely helpful to assess the individualised risk of UTI in clinical practice by incorporating the presentation of simple clinical data.