机器学习模型的构建和测试:预测癌症发生率和死亡率
1School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK.
Diseases (Basel, Switzerland)
|July 26, 2024
概括
机器学习模型使用广泛的数据集预测癌症发病率和死亡率. 这种方法有助于公共卫生政策和可持续的医疗保健规划.
科学领域:
- 环境健康 环境健康
- 在瘤学瘤学.
- 数据科学是数据科学.
背景情况:
- 越来越多的环境挑战与癌症发病率的上升有关.
- 准确预测癌症发病率和死亡率对于公共卫生政策至关重要.
- 机器学习为了解癌症动态提供了一种新的方法.
研究的目的:
- 开发和评估用于预测癌症发病率和死亡率的机器学习框架.
- 在全面的癌症数据集上评估各种机器学习算法的性能.
- 通过准确的癌症预测,为公共卫生政策和可持续的医疗保健规划提供信息.
主要方法:
- 利用了72 591个记录的数据集,其中包括年龄,病例数,人口规模,种族,性别,诊断地点和年份等变量.
- 采用各种机器学习算法:决策树,随机森林,后勤回归,支持矢量机器和神经网络.
- 基于测试准确度分析模型性能.
主要成果:
- 测试准确度达到62.17% (决策树),61.92% (随机森林),54.53% (逻辑回归),55.72% (支持向量机器) 和62.30% (神经网络).
- 神经网络和决策树在测试模型中显示出最高的预测准确性.
- 该框架为预测未来的癌症发病率和死亡率提供了强大的能力.
结论:
- 开发的机器学习框架增强了对癌症动态的理解,并使精确的预测成为可能.
- 这种方法支持研究人员和政策制定者在为公共卫生做出明智决策.
- 该框架的应用通过考虑长期的生态和社会影响,促进了可持续的医疗保健规划.
相关概念视频
01:21Cancer Survival Analysis
336
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
336
02:43Mouse Models of Cancer Study
5.5K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
5.5K
01:25Statistical Methods for Analyzing Epidemiological Data
344
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
344
01:18Steps in Outbreak Investigation
119
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
119
01:24Kaplan-Meier Approach
119
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
119
02:59Cancer Prevention
6.1K
Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
Some...
Some...
6.1K