Fayez, MustafaKurnaz, Sefer2025-02-062025-02-0620212190-55092190-5517https://doi.org/10.1007/s13204-021-01990-6https://hdl.handle.net/20.500.12939/5227Machine learning (ML) is also seen as an advanced technique that is only usable by highly qualified specialists. This prohibits this instrument from being utilized by many doctors and biologists in their studies. This paper's purpose is to eradicate this obsolete perception. We claim that the recent creation of advanced high-performance ML techniques helps biomedical researchers to create competitive ML models rapidly without needing in-depth knowledge of the algorithms underlying them. This advanced system is implemented used best programming tool Python including two parts. Firstly, feature engineering and preprocessing with the Neighborhood Cleaning Rule (NCL) high-performance re-sampling procedure. Second, advanced models for high-performance machine learning, including AutoML, advanced XGBoost, and advanced ensemble bagging models. Finally, we believe that our developments would improve the way doctors interpret machine learning utilizing sophisticated and high-performance machine learning technologies and facilitate broad clinical use of Artificial Intelligence (AI) techniques.eninfo:eu-repo/semantics/openAccessDiagnosis diseasesNCLAdvanced Bagging modelsAdvanced XGBoostAutoMLPythonArticle; Early Access; Retracted Publication10.1007/s13204-021-01990-62-s2.0-85112045665Q1WOS:000683237600001