Application of machine learning algorithm in clinical diagnosis and survival prognosis analysis of lung cancer
- VernacularTitle:机器学习算法在肺癌临床诊断及生存预后分析中的应用
- Author:
Jiaxin XU
1
,
2
;
Kai QIAN
3
,
4
;
Lihong JIANG
3
,
4
Author Information
1. Department of Cardiothoracic Surgery, Yan'
2. an Hospital Affiliated to Kunming Medical University, Kunming, 650051, P. R. China
3. Department of Thoracic Surgery, Yunnan First People'
4. s Hospital, Kunming, 650051, P. R. China
- Publication Type:Journal Article
- Keywords:
Lung cancer;
machine learning;
neural network;
support vector machine;
review
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2022;29(06):777-781
- CountryChina
- Language:Chinese
-
Abstract:
Lung cancer is one of the tumors with the highest incidence rate and mortality rate in the world. It is also the malignant tumor with the fastest growing number of patients, which seriously threatens human life. How to improve the accuracy of diagnosis and treatment of lung cancer and the survival prognosis is particularly important. Machine learning is a multi-disciplinary interdisciplinary specialty, covering the knowledge of probability theory, statistics, approximate theory and complex algorithm. It uses computer as a tool and is committed to simulating human learning methods, and divides the existing content into knowledge structures to effectively improve learning efficiency and being able to integrate computer science and statistics into medical problems. Through the introduction of algorithm to absorb the input data, and the application of computer analysis to predict the output value within the acceptable accuracy range, identify the patterns and trends in the data, and finally learn from previous experience, the development of this technology brings a new direction for the diagnosis and treatment of lung cancer. This article will review the performance and application prospects of different types of machine learning algorithms in the clinical diagnosis and survival prognosis analysis of lung cancer.
