A disease diagnosis and treatment recommendation system based on big data mining and cloud computing

被引:116
作者
Chen, Jianguo [1 ]
Li, Kenli [1 ,2 ]
Rong, Huigui [1 ]
Bilal, Kashif [3 ]
Yang, Nan [4 ]
Li, Keqin [1 ,5 ]
机构
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Hunan, Peoples R China
[2] Natl Supercomp Ctr Changsha, Changsha 410082, Hunan, Peoples R China
[3] Comsats Inst Informat Technol, Abbottabad 45550, Pakistan
[4] Xi An Jiao Tong Univ, Affiliated Hosp 2, Xian 710049, Shaanxi, Peoples R China
[5] SUNY Coll New Paltz, Dept Comp Sci, New Paltz, NY 12561 USA
基金
中国国家自然科学基金;
关键词
Big data mining; Cloud computing; Disease diagnosis and treatment; Recommendation system; EVIDENCE-BASED MEDICINE; CLUSTERING-ALGORITHM; MAPREDUCE; SEARCH;
D O I
10.1016/j.ins.2018.01.001
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and cooperative actions in disease diagnoses and treatments between different departments and hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors might have difficulty in identifying them promptly and accurately. Therefore, to maximize the utilization of the advanced medical technology of developed hospitals and the rich medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in this paper. First, to effectively identify disease symptoms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (DD) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm separately. The appropriate diagnosis and treatment schemes are recommended for patients and inexperienced doctors, even if they are in a limited therapeutic environment. Moreover, to reach the goals of high performance and low latency response, we implement a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experimental results demonstrate that the proposed DDTRS realizes disease-symptom clustering effectively and derives disease treatment recommendations intelligently and accurately. (C) 2018 Elsevier Inc. All rights reserved.
引用
收藏
页码:124 / 149
页数:26
相关论文
共 50 条
[31]   Research on Algorithm of Vehicle Track Data Mining Based on Cloud Computing [J].
Sun Hailong ;
Li Fangsong .
AGRO FOOD INDUSTRY HI-TECH, 2017, 28 (01) :1439-1443
[32]   <bold>Data mining in Cloud Computing </bold> [J].
Geng, Xia ;
Yang, Zhi .
PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND COMPUTER APPLICATIONS (ICSA 2013), 2013, 92 :1-7
[33]   Research on Data Mining Algorithm Based on Cloud Computing [J].
Mai Xiao-Dong .
AGRO FOOD INDUSTRY HI-TECH, 2017, 28 (03) :1752-1756
[34]   Analysis of the Data Mining Application Based on Cloud Computing [J].
Li Xiaohui ;
Liu Wei ;
Wang Lele .
ADVANCED DESIGN AND MANUFACTURING TECHNOLOGY III, PTS 1-4, 2013, 397-400 :2426-2429
[35]   Cloud Computing Based Data Mining of Medical Information [J].
Wang, Lihua ;
Zhang, Ze .
PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND MANAGEMENT INNOVATION, 2015, 28 :978-982
[36]   Design of big data processing system architecture based on Hadoop Under the cloud computing [J].
Duan, Chunmei .
MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY, 2014, 556-562 :6302-6306
[37]   Enterprise financial management information system based on cloud computing in big data environment [J].
Chen, Xuanjun ;
Metawa, N. .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 39 (04) :5223-5232
[38]   Application of Cloud Computing and Data Mining in Smart Tourism System [J].
Fan Tongke .
PROCEEDINGS OF THE SECOND INTERNATIONAL SYMPOSIUM - MANAGEMENT, INNOVATION AND DEVELOPMENT, 2015, :141-144
[39]   Minimizing Big Data Problems using Cloud Computing Based on Hadoop Architecture [J].
Adnan, Muhammad ;
Afzal, Muhammad ;
Aslam, Muhammad ;
Jan, Roohl ;
Martinez-Enriquez, A. M. .
2014 11TH ANNUAL HIGH CAPACITY OPTICAL NETWORKS AND EMERGING/ENABLING TECHNOLOGIES (PHOTONICS FOR ENERGY), 2014, :99-103
[40]   Application of Big Data Analytics via Cloud Computing [J].
Yetis, Yunus ;
Sara, Ruthvik Goud ;
Erol, Berat A. ;
Kaplan, Halid ;
Akuzum, Abdurrahman ;
Jamshidi, Mo .
2016 WORLD AUTOMATION CONGRESS (WAC), 2016,