[1]苗立志,刁继尧,娄 冲,等.基于 Spark 和随机森林的乳腺癌风险预测分析[J].计算机技术与发展,2019,29(08):142-146.[doi:10. 3969 / j. issn. 1673-629X. 2019. 08. 027]
　MIAO Li-zhi,DIAO Ji-yao,LOU Chong,et al.Breast Cancer Risk Prediction Analysis Based on Apache Spark and Random Forest Algorithm[J].,2019,29(08):142-146.[doi:10. 3969 / j. issn. 1673-629X. 2019. 08. 027]
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基于 Spark 和随机森林的乳腺癌风险预测分析()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年08期
页码:
142-146
栏目:
应用开发研究
出版日期:
2019-08-10

文章信息/Info

Title:
Breast Cancer Risk Prediction Analysis Based on Apache Spark and Random Forest Algorithm
文章编号:
1673-629X(2019)08-0135-05
作者:
苗立志1; 2; 3 ; 刁继尧4 ; 娄 冲4 ; 崔进东4
1. 南京邮电大学 地理与生物信息学院,江苏 南京 210023; 2. 南京邮电大学 江苏省智慧健康大数据分析与位置服务工程实验室,江苏 南京 210023; 3. 南京邮电大学 泛在网络健康服务系统教育部工程研究中心,江苏 南京 210003; 4. 南京邮电大学 通信与信息工程学院,江苏 南京 210003
Author(s):
MIAO Li-zhi1; 2; 3 ; DIAO Ji-yao 4 ; LOU Chong 4 ; CUI Jin-dong 4
1. School of Geographical and Biological Information,Nanjing University of Posts and Telecommunications,Nanjing 210023,China; 2. Jiangsu Engineering Laboratory for Smart Analysis of Healthy Big Data and Location Based Services,Nanjing University of Posts
关键词:
Apache Spark; 随机森林; 疾病预测; 机器学习; 智能健康; 大数据分析
Keywords:
Apache Spark; random forest; disease prediction; machine learning; intelligent health; big data analysis
分类号:
TP311
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 08. 027
摘要:
现代医疗正在朝着智能健康的方向发展。 在此大背景下,为了提高乳腺癌风险的发现及预测效果,文中采用大数据分析技术并基于随机森林模型,应用多个弱分类器,将多个决策树获得的结果进行集成,得到疾病发病概率;并采用管道学习方法来训练模型,基于该模型开展了致病因素分析以及结果预测。 同时,通过皮尔逊相关系数和 Spearman 等级相关系数来进行相关度分析,研究权重较高的影响因子,提高乳腺癌风险的监测和早期预防。 实验结果表明,在乳腺癌致病细胞细胞核的相关参数中,Perimeter、Texture 和 Concave points 影响因子对于乳腺癌的致病影响程度较大,更易导致疾病的发生。 基于管道训练方法所建立的模型预测精度可达 99.04%,精度高、方法可靠。 最终的实验研究结果对于乳腺癌风险的发现具有一定程度的参考意义。
Abstract:
Modern medicine is developing towards intelligent health. Under this background,to improve the detection and prediction of breast cancer risk,we use big data analysis and multiple weak classifiers based on random forest model to integrate the results of decision trees to obtain incidence of disease. The pipeline learning method is used to train the model. We also carry out pathogenic factor analysis and result prediction based on the pipeline learning. Meanwhile,the influencing factors with higher weight are studied by Pearson correlation coefficient and Superman rank correlation coefficient,to improve the monitoring risk of breast cancer. The experiment shows that among the relevant parameters of the nucleus of breast cancer pathogenic cells,the Perimeter,Texture and Concave points have a greater impact on the pathogenesis of breast cancer and are more likely to cause the lead to the disease. The prediction accuracy of the model based on the pipeline training method can reach 99.04%,which will provide a certain reference for the discovery of breast cancer risk.

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更新日期/Last Update: 2019-08-10