Machine 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.

机器学习 (ML) 也被视为一种只有高素质专家才能使用的先进技术。这禁止许多医生和生物学家在他们的研究中使用该仪器。本文的目的是消除这种过时的观念。我们声称，最近创建的高级高性能 ML 技术可帮助生物医学研究人员快速创建具有竞争力的 ML 模型，而无需深入了解其背后的算法。这个先进的系统是使用最好的编程工具 Python 实现的，包括两部分。首先，使用邻域清理规则 (NCL) 高性能重采样程序进行特征工程和预处理。其次，用于高性能机器学习的高级模型，包括 AutoML、高级 XGBoost 和高级集成装袋模型。最后，