An Application-Based Review of Recent Advances of Data Mining in Healthcare
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Abstract
Background: Data mining as an integral part of the knowledge discovery in database (KDD) has gained significant attention over the past few years. By and large, data mining is the process of finding interesting structures in a considerably voluminous amount of data. Owing to its methods and algorithms supporting variable types of data, the data mining approach has been applied in many scientific areas, including the healthcare industry.
Regarding this matter, in this paper, we elaborate on the latest papers, including data mining techniques and algorithms in the healthcare field of research.
Results: We present a data mining review based on the newest researches. Afterward, we categorize data mining papers in healthcare based on supervised and unsupervised learning paradigms as well as classifying them in terms of their applications in the healthcare domain.
Conclusions: In every healthcare application, we propose some summary points of the papers. At last, we delve into the absence and hence, the necessity of existing some novel methods in healthcare domains in this researches.
1.P. Ahmad, “Techniques of Data Mining In Healthcare : A Review,” vol. 120, no. 15, pp. 38–50, 2015.
2.N. Jothi, N. Aini, A. Rashid, and W. Husain, “Data Mining in Healthcare – A Review,” Procedia - Procedia Comput. Sci., vol. 72, pp. 306–313, 2015.
3.R. J. Roiger, Data Mining A Tutorial-Based Primer (Second Edition), Second. Taylor & Francis Group, 2016.
4.C. Fan, F. Xiao, Z. Li, and J. Wang, “Unsupervised data analytics in mining big building operational data for energy efficiency enhancement : A review,” Energy Build., vol. 159, pp. 296–308, 2018.
5.S. García, Intelligent Systems Reference Library 72 Data Preprocessing in Data Mining. .
6.S. Patel and H. Patel, “SURVEY OF DATA MINING TECHNIQUES USED IN HEALTHCARE DOMAIN Sheenal,” Int. J. Inf. Sci. Tech., vol. 6, no. 1, pp. 53–60, 2016.
7.D. S. Vijayarani S, “Data Mining Classification Algorithms for Kidney Disease Prediction,” Int. J. Cybern. Informatics, vol. 4, no. 4, pp. 13–25, 2015.
8.U. Shafique and L. Campus, “Data Mining in Healthcare for Heart Diseases Data Mining in Healthcare for Heart Diseases,” no. March, 2015.
9.N. M. Saravana, T. Eswari, P. Sampath, and S. Lavanya, “Predictive Methodology for Diabetic Data Analysis in Big Data,” Procedia - Procedia Comput. Sci., vol. 50, pp. 203–208, 2015.
10.I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, “Machine Learning and Data Mining,” Comput. Struct. Biotechnol. J., 2016.
11.Y. Li, C. Bai, and C. K. Reddy, “A distributed ensemble approach for mining healthcare data under privacy constraints,” Inf. Sci. (Ny)., 2015.
12.S. Perveen, M. Shahbaz, A. Guergachi, and K. Keshavjee, “Performance Analysis of Data Mining Classification Techniques to Predict Diabetes,” Procedia - Procedia Comput. Sci., vol. 82, no. March, pp. 115–121, 2016.
13.E. W. Johansson et al., “Integrated paediatric fever management and antibiotic over ‑ treatment in Malawi health facilities : data mining a national facility census,” Malar. J., pp. 1–12, 2016.
14.S. Huda, J. Yearwood, H. F. Jelinek, M. M. Hassan, and M. Buckland, “A hybrid feature selection with ensemble classification for imbalanced healthcare data : A case study for brain tumor diagnosis,” vol. 3536, no. c, pp. 1–13, 2016.
15.S. Sung et al., “Developing a stroke severity index based on administrative data was feasible using data mining techniques,” J. Clin. Epidemiol., 2015.
16.J. Gonçalves, L. P. Reis, and V. Carvalho, “Data Mining and Electronic Devices applied to Quality of Life Related to Health Data.”
17.M. Scotch, K. L. Smith, and G. Gonzalez, “Social media mining for public health monitoring and surveillance,” pp. 468–479, 2016.
18.V. Rawte, “Fraud Detection in Health Insurance using Data Mining Techniques,” 2015.
19.G. Sprint, D. Cook, R. Fritz, and M. Schmitter-edgecombe, “Detecting Health and Behavior Change by Analyzing Smart Home Sensor Data,” pp. 1–3, 2016.
20.A. Yassine, S. Singh, and A. A. Member, “Mining Human Activity Patterns from Smart Home Big Data for Healthcare Applications,” vol. 3536, no. c, pp. 1–10, 2017.
21.K. J. Serrano, K. I. Coa, M. Yu, D. L. Wolff-hughes, and A. A. Atienza, “Characterizing user engagement with health app data: a data mining approach,” Transl. Behav. Med., pp. 277–285, 2015.
22.K. J. Serrano, M. Yu, K. I. Coa, L. M. Collins, and A. A. Atienza, “Mining Health App Data to Find More and Less Successful Weight Loss Subgroups,” J. Med. Internet Res., vol. 18, pp. 1–11, 2016.
23.K. Lin, F. Xia, W. Wang, D. Tian, and J. Song, “System Design for Big Data Application in Emotion-aware Healthcare,” IEEE Access, vol. 3536, no. c, pp. 1–9, 2016.
24.P. S. Mathew, “Big Data Solutions in Healthcare : Problems and Perspectives,” 2015.
25.J. Hu, A. Perer, and F. Wang, “Data Driven Analytics for Personalized Healthcare.”
26.H. Joudaki, A. Rashidian, B. Minaei-bidgoli, M. Mahmoodi, and B. Geraili, “Using Data Mining to Detect Health Care Fraud and Abuse : A Review of Literature,” vol. 7, no. 1, pp. 194–202, 2015.
27.C. H. Lee and H. Yoon, “Medical big data : promise and challenges,” vol. 2017, no. 1, pp. 3–11, 2017.
28.E. Rojas, J. Munoz-gama, M. Sepúlveda, and D. Capurro, “Process mining in healthcare : A literature review,” J. Biomed. Inform., vol. 61, pp. 224–236, 2016.
29.A. B. G. Xiaojin Zhu, Introduction to Semi-Supervised Learning, First. Morgan & Claypool Publishers, 2009.
2.N. Jothi, N. Aini, A. Rashid, and W. Husain, “Data Mining in Healthcare – A Review,” Procedia - Procedia Comput. Sci., vol. 72, pp. 306–313, 2015.
3.R. J. Roiger, Data Mining A Tutorial-Based Primer (Second Edition), Second. Taylor & Francis Group, 2016.
4.C. Fan, F. Xiao, Z. Li, and J. Wang, “Unsupervised data analytics in mining big building operational data for energy efficiency enhancement : A review,” Energy Build., vol. 159, pp. 296–308, 2018.
5.S. García, Intelligent Systems Reference Library 72 Data Preprocessing in Data Mining. .
6.S. Patel and H. Patel, “SURVEY OF DATA MINING TECHNIQUES USED IN HEALTHCARE DOMAIN Sheenal,” Int. J. Inf. Sci. Tech., vol. 6, no. 1, pp. 53–60, 2016.
7.D. S. Vijayarani S, “Data Mining Classification Algorithms for Kidney Disease Prediction,” Int. J. Cybern. Informatics, vol. 4, no. 4, pp. 13–25, 2015.
8.U. Shafique and L. Campus, “Data Mining in Healthcare for Heart Diseases Data Mining in Healthcare for Heart Diseases,” no. March, 2015.
9.N. M. Saravana, T. Eswari, P. Sampath, and S. Lavanya, “Predictive Methodology for Diabetic Data Analysis in Big Data,” Procedia - Procedia Comput. Sci., vol. 50, pp. 203–208, 2015.
10.I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, “Machine Learning and Data Mining,” Comput. Struct. Biotechnol. J., 2016.
11.Y. Li, C. Bai, and C. K. Reddy, “A distributed ensemble approach for mining healthcare data under privacy constraints,” Inf. Sci. (Ny)., 2015.
12.S. Perveen, M. Shahbaz, A. Guergachi, and K. Keshavjee, “Performance Analysis of Data Mining Classification Techniques to Predict Diabetes,” Procedia - Procedia Comput. Sci., vol. 82, no. March, pp. 115–121, 2016.
13.E. W. Johansson et al., “Integrated paediatric fever management and antibiotic over ‑ treatment in Malawi health facilities : data mining a national facility census,” Malar. J., pp. 1–12, 2016.
14.S. Huda, J. Yearwood, H. F. Jelinek, M. M. Hassan, and M. Buckland, “A hybrid feature selection with ensemble classification for imbalanced healthcare data : A case study for brain tumor diagnosis,” vol. 3536, no. c, pp. 1–13, 2016.
15.S. Sung et al., “Developing a stroke severity index based on administrative data was feasible using data mining techniques,” J. Clin. Epidemiol., 2015.
16.J. Gonçalves, L. P. Reis, and V. Carvalho, “Data Mining and Electronic Devices applied to Quality of Life Related to Health Data.”
17.M. Scotch, K. L. Smith, and G. Gonzalez, “Social media mining for public health monitoring and surveillance,” pp. 468–479, 2016.
18.V. Rawte, “Fraud Detection in Health Insurance using Data Mining Techniques,” 2015.
19.G. Sprint, D. Cook, R. Fritz, and M. Schmitter-edgecombe, “Detecting Health and Behavior Change by Analyzing Smart Home Sensor Data,” pp. 1–3, 2016.
20.A. Yassine, S. Singh, and A. A. Member, “Mining Human Activity Patterns from Smart Home Big Data for Healthcare Applications,” vol. 3536, no. c, pp. 1–10, 2017.
21.K. J. Serrano, K. I. Coa, M. Yu, D. L. Wolff-hughes, and A. A. Atienza, “Characterizing user engagement with health app data: a data mining approach,” Transl. Behav. Med., pp. 277–285, 2015.
22.K. J. Serrano, M. Yu, K. I. Coa, L. M. Collins, and A. A. Atienza, “Mining Health App Data to Find More and Less Successful Weight Loss Subgroups,” J. Med. Internet Res., vol. 18, pp. 1–11, 2016.
23.K. Lin, F. Xia, W. Wang, D. Tian, and J. Song, “System Design for Big Data Application in Emotion-aware Healthcare,” IEEE Access, vol. 3536, no. c, pp. 1–9, 2016.
24.P. S. Mathew, “Big Data Solutions in Healthcare : Problems and Perspectives,” 2015.
25.J. Hu, A. Perer, and F. Wang, “Data Driven Analytics for Personalized Healthcare.”
26.H. Joudaki, A. Rashidian, B. Minaei-bidgoli, M. Mahmoodi, and B. Geraili, “Using Data Mining to Detect Health Care Fraud and Abuse : A Review of Literature,” vol. 7, no. 1, pp. 194–202, 2015.
27.C. H. Lee and H. Yoon, “Medical big data : promise and challenges,” vol. 2017, no. 1, pp. 3–11, 2017.
28.E. Rojas, J. Munoz-gama, M. Sepúlveda, and D. Capurro, “Process mining in healthcare : A literature review,” J. Biomed. Inform., vol. 61, pp. 224–236, 2016.
29.A. B. G. Xiaojin Zhu, Introduction to Semi-Supervised Learning, First. Morgan & Claypool Publishers, 2009.
| Files | ||
| Issue | Vol 5 No 4 (2019) | |
| Section | Review Article(s) | |
| DOI | https://doi.org/10.18502/jbe.v5i4.3864 | |
| Keywords | ||
| Data mining; Health care; Learning paradigm; Supervised; Unsupervised; Knowledge discovery | ||
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How to Cite
1.
Shirazi S, Baziyad H, Karimi H. An Application-Based Review of Recent Advances of Data Mining in Healthcare. JBE. 2020;5(4):268-278.
