首页|Cracow University of Technology Reports Findings in Colon Cancer (A Novel Approa ch for Predicting the Survival of Colorectal Cancer Patients Using Machine Learn ing Techniques and Advanced Parameter Optimization Methods)
Cracow University of Technology Reports Findings in Colon Cancer (A Novel Approa ch for Predicting the Survival of Colorectal Cancer Patients Using Machine Learn ing Techniques and Advanced Parameter Optimization Methods)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Colon Cance r is the subject of a report. According to news originating from Krakow, Poland, by NewsRx correspondents, research stated, “Colorectal cancer is one of the mos t prevalent forms of cancer and is associated with a high mortality rate. Additi onally, an increasing number of adults under 50 are being diagnosed with the dis ease.” Our news journalists obtained a quote from the research from the Cracow Universi ty of Technology, “This underscores the importance of leveraging modern technolo gies, such as artificial intelligence, for early diagnosis and treatment support . Eight classifiers were utilized in this research: Random Forest, XGBoost, CatB oost, LightGBM, Gradient Boosting, Extra Trees, the k-nearest neighbor algorithm (KNN), and decision trees. These algorithms were optimized using the frameworks Optuna, RayTune, and HyperOpt. This study was conducted on a public dataset fro m Brazil, containing information on tens of thousands of patients. The models de veloped in this study demonstrated high classification accuracy in predicting on e-, three-, and five-year survival, as well as overall mortality and cancer-spec ific mortality. The CatBoost, LightGBM, Gradient Boosting, and Random Forest cla ssifiers delivered the best performance, achieving an accuracy of approximately 80% across all the evaluated tasks.”
KrakowPolandEuropeCancerColon Ca ncerColorectal ResearchCyborgsEmerging TechnologiesGastroenterologyHea lth and MedicineMachine LearningOncology
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