![]() The UAT ensures the quality of the product in the early phase of the development and implementation of the projects. The study's findings were corroborated by the output data from the UAT cases. The proposed UAT has significantly better production time, development cost, and software quality in comparison to other traditional UATs. The proposed UAT will provide an optimal solution in the software testing phases implemented earlier than black-box testing. A Modified Reuse of Code (MRC) is proposed for a feasible time-saving solution. A High Capability to Detect (HCD) procedure has been incorporated in the problem formulation that has optimally identified sensitive bugs. Additionally, it is devised to maximise time reduction by implementing the client testing in all the three processes. The Software Development Life Cycle (SDLC) was adapted to develop software and introduce the UAT process right from the initial phase of the software development. Thus, this research focuses on optimal implementation of the User Acceptance Testing (UAT) and the process generation integration. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, in order to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan.įast-growing software needs result in the rise of quality software in technical and time challenges in software development and the impact the cost and scarcity of resources addressed by the companies. ML algorithms with re-sampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an AUC of 0.77 in the validation cohort. Based on maximising sensitivity (true positives), the ‘hero’ model was the cost-sensitive Random Forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m=114 predictive features. ![]() Re-sampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on performance in the validation dataset, the Logistic Model Tree, Random Forest and Naïve Bayes models were taken forward to cost-sensitive learning optimisation.ġ92 patients experienced acute desquamation. Using demographic and treatment-related features (m=122) from patients (n=2,058) at 26 centres, we trained eight ML algorithms with 10-fold cross-validation in a 50:50 random-split dataset with class stratification to predict acute breast desquamation. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation following whole breast external beam radiotherapy in the prospective multi-centre XXXXXXX cohort study. There is a paucity of validated clinical prediction models for radiation toxicity. Some breast cancer patients treated by surgery and radiotherapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life.
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