没有合适的资源?快使用搜索试试~ 我知道了~
首页利用数据挖掘技术的心脏病早期预测模型研究
"这篇研究论文探讨了如何使用数据挖掘技术对心脏病的早期预测。作者Vikas Chaurasia和Saurabh Pal通过CART、ID3和决策表等数据挖掘算法,开发预测心脏病生存能力的模型,并采用了10倍交叉验证进行评估。研究指出,心脏病是全球主要的死亡原因,尤其在印度南部的比例最高。" 心脏病已经成为全球第一大死亡原因,尤其在25-69岁的年龄段,大约四分之一的死亡与心脏病有关。如果考虑所有年龄层,这一比例约为19%。无论性别和地区,心脏病都是首要的死因。在印度不同地区,心脏病导致的死亡率差异显著,南印度的比率最高,达到了25%,而中印度则为12%。 为了应对这一挑战,研究者们利用数据挖掘技术来预测心脏病的生存能力。他们选择了三种常见的数据挖掘算法:分类和回归树(CART)、迭代二分法3(ID3)以及从决策树或基于规则的分类器中提取的决策表(DT)。这些算法能够从大规模数据集中构建预测模型,帮助识别心脏病风险因素并提前预警。 在模型构建过程中,10倍交叉验证被用于提供无偏的性能估计。这是一种统计学方法,它将数据集分成10个子集,模型在9个子集上训练,在剩下的1个子集上测试,重复10次,确保每个子集都被用作一次测试集。这样可以确保模型的泛化能力和避免过拟合,从而提高预测的准确性和可靠性。 该研究论文发表在Caribbean Journal of Science and Technology上,强调了数据挖掘在医学预测领域的潜力,特别是在心脏病早期识别和预防方面。通过这些技术,研究人员能够发现潜在的风险因素,有助于提前干预,从而改善患者的生活质量和降低心脏病的死亡率。
资源详情
资源推荐
![](https://csdnimg.cn/release/download_crawler_static/18895352/bg1.jpg)
Electronic copy available at: https://ssrn.com/abstract=2991237
Research Article Vikas Chaurasia, et al, Carib.j.SciTech,2013,Vol.1,208-217
Early Prediction of Heart Diseases Using Data Mining
Techniques
Authors & Affiliation:
Vikas Chaurasia
Research Scholar, Sai Nath
University, Ranchi, Jharkhand,
India.
Saurabh Pal
Head, Dept. of MCA,
VBS Purvanchal University,
Jaunpur, India
Correspondence To:
Vikas Chaurasia
Keywords:
Heart disease, Survivability,
Data Mining, CART, ID3,
Decision table.
© 2013. The Authors.
Published under Caribbean
Journal of Science and
Technology
ISSN 0799-3757
http://caribjscitech.com/
ABSTRACT
Largest-
ever study of deaths shows heart diseases have emerged as the
number one killer in world. About 25 per cent of deaths in the age group
of 25-
69 years occur because of heart diseases. If all age groups are
included
, heart diseases account for about 19 per cent of all deaths. It is
the leading cause of death among males as well as females. It is also the
leading cause of death in all regions though the numbers vary. The
proportion of deaths caused by heart disease is
the highest in south India
(25 per cent) and lowest - 12 per cent - in the central region of India.
The prediction of heart disease survivability has been a challenging
research problem for many researchers. Since the early dates of the
related research,
much advancement has been recorded in several related
fields. Therefore, the main objective of this manuscript is to report on a
research project where we took advantage of those available
technological advancements to develop prediction models for heart
disease survivability.
We used three popular data mining algorithms CART (Classification
and Regression Tree), ID3 (Iterative Dichotomized 3) and decision table
(DT) extracted from a decision tree or rule-based classifier to develop
the prediction models using a large dataset. We also used 10-fold cross-
validation methods to measure the unbiased estimate.
Electronic copy available at: https://ssrn.com/abstract=2991237
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
安全验证
文档复制为VIP权益,开通VIP直接复制
![](https://csdnimg.cn/release/wenkucmsfe/public/img/green-success.6a4acb44.png)