智能医疗保健中的预测分析用于使用机器学习方法预测儿童死亡率
Farrukh Iqbal1, Muhammad Islam Satti2,3, Azeem Irshad4
1Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Karachi, Pakistan.
Open life sciences
|July 19, 2023
概括
预测分析准确地确定了巴基斯坦五岁以下儿童死亡率的关键风险因素. 随机森林模型达到93.8%的准确性,有助于儿童健康干预.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 儿童死亡率仍然是发展中国家的一个关键问题,巴基斯坦面临着高低的五岁以下儿童死亡率 (69/1000活产).
- 可持续发展目标3 (SDG3) 旨在将全球五岁以下儿童死亡率降至25/1000活产.
- 预测分析为个性化医疗保健和有针对性的干预提供了变革潜力.
研究的目的:
- 开发和评估巴基斯坦五岁以下儿童死亡率的预测分析框架.
- 通过机器学习识别影响儿童死亡率的关键风险因素.
- 评估各种监督学习分类器的性能,以预测死亡率.
主要方法:
- 利用了巴基斯坦人口和健康调查 (2017-2018) 数据集.
- 采用多重归算用于缺少的数据和信息获取用于特征选择.
- 应用合成少数人过量采样技术 (SMOTE) 用于数据集平衡.
- 训练并比较决策树,随机森林,天真贝叶斯和极端梯度提升分类器.
主要成果:
- 确定了关键风险因素:五岁以下儿童的数量,分娩间隔,家庭规模,母亲年龄,第一次分娩的年龄,产前护理,母乳养,出生大小和分娩地点.
- 随机森林分类器表现出卓越的性能,准确率为93.8%,精度为0.964,回忆率为0.971,F1得分为0.967.
- 建立了一个功能预测框架,以预测儿童生存状况.
结论:
- 预测分析,特别是随机森林模型,在预测巴基斯坦五岁以下儿童死亡率方面非常有效.
- 鉴定的风险因素为针对性儿童健康干预提供了宝贵的见解.
- 开发的框架可以显著支持儿童健康计划的决策.
更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.3K
12:18A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
7.6K
相关概念视频
Steps in Outbreak Investigation
152
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:
152
Cancer Survival Analysis
390
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...
390
Kaplan-Meier Approach
183
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,...
183
Issues And Trends In Healthcare Delivery System
5.7K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.7K
Applications of Life Tables
93
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
93
Actuarial Approach
98
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
98
