医学机器学习：数据预处理、超参数调优与模型比较的实用分析

医学机器学习：数据预处理、超参数调优与模型比较的实用分析

引用

CSDN

1.

https://blog.csdn.net/kkiron/article/details/144050409

摘要

本文介绍了医学中的机器学习，重点阐述了数据预处理、超参数调优和模型比较的技术。在数据预处理方面，包括数据收集与整理、处理缺失值、特征工程等内容，以确保数据质量和可用性。超参数调优对模型性能至关重要，介绍了多种调优方法及其优缺点和适用场景。模型比较在医学领域具有重要意义，通过比较不同模型可选择最适合特定任务的模型，提高诊断准确性和治疗有效性。最后对医学机器学习的未来发展进行了展望。

关键词

医学机器学习；数据预处理；超参数调优；模型比较

ABSTRACT

This paper introduces machine learning in medicine, focusing on the techniques of data preprocessing, hyperparameter tuning, and model comparison. In terms of data preprocessing, it includes data collection and collation, handling missing values, feature engineering, etc., to ensure data quality and usability. Hyperparameter tuning is crucial for model performance, and various tuning methods, their advantages and disadvantages, and applicable scenarios are introduced. Model comparison is of great significance in the medical field, allowing the selection of the most suitable model for specific tasks to improve diagnostic accuracy and treatment effectiveness. Finally, the future development of medical machine learning is prospected.