Research on the Application of Machine Learning in Cancer Prediction and Diagnosis

Abstract

Due to the continuous development and advancement of artificial intelligence technology, the application of machine learning in the medical field, particularly in cancer treatment and prediction, is becoming increasingly widespread and profound. By utilizing machine learning algorithms, healthcare professionals can analyze large volumes of medical data more accurately through specific models. This paper discusses the application of machine learning in cancer diagnosis and treatment, with special attention to prediction, diagnosis and treatment. By analyzing a large number of clinical and genomic data, machine learning, which provides support for early diagnosis, reveals the complex pattern of cancer development. Deep learning technology can help doctors diagnose and predict cancer more accurately in medical image analysis and genome data analysis. Ensemble learning methods such as the random forest model improve the accuracy of prediction. It also introduces semi supervised learning, which provides a new perspective for cancer prediction: enhancing model training by using unlabeled data. The paper also introduces the application of cox-net and survival support vector machine in survival analysis.