Analysis of the Basic Concepts and Applications of Machine Learning in the Medical Field

Abstract

The application of machine learning in cancer prediction has important academic value and practical significance, and can make important contributions to improving the accuracy and efficiency of early cancer detection. The article explores the key concepts of contemporary machine learning, the processes and steps involved in data processing, and its application in cancer prediction. It particularly focuses on the accuracy of different machine learning models in predicting specific cancers and their characteristics in medical applications. Due to its ability to handle large-scale and complex data, machine learning has become a crucial tool for improving the accuracy of early cancer detection. The article provides a detailed introduction to supervised learning models (such as SVM, decision trees, logistic regression) and unsupervised learning models (such as clustering analysis, PCA), discussing their concepts and conditions of application. The article also describes the performance of these models in breast cancer classification tasks, finding that SVM performed the best in terms of accuracy. Despite the significant potential of machine learning in medical prediction, challenges such as noise and missing values in clinical data, model interpretability, and personalized prediction remain. Future research should focus on improving data preprocessing methods, enhancing model interpretability, and promoting its application in clinical practice.