Smart Healthcare Support Using Data Mining and Machine Learning
Ever since the first cities were created, they have been dependent on technology to sustain life. The smart city paradigm integrates advanced monitoring, sensing, communication, and control technologies, aiming at providing real-time, interactive, and intelligent city services to citizens. Thus, the healthcare sector, as an essential part of our lives, could not remain unaffected. The advances in technology provided great potential for many aspects of the health system. In this chapter, we focus on popular machine learning (ML) and data mining (DM) predictive and descriptive techniques and their most prominent applications in the healthcare domain. First, we introduce the process of mining data to extract healthcare relevant knowledge. We briefly review key techniques used, including classification, clustering, and association rule mining. We then focus on specific smart healthcare applications including but not limited to (i) assisting diagnosis and treatment, (ii) health management, (iii) disease prevention and risk monitoring, (iv) virtual assistant and wearable sensors, and (v) drug research. The chapter concludes with a running example of applying well-known ML and DM techniques to a publicly available dataset related to diabetes and a discussion on the impact of DM in healthcare support.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Subscribe and save
Springer+ Basic
€32.70 /Month
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
Price includes VAT (France)
eBook EUR 117.69 Price includes VAT (France)
Softcover Book EUR 158.24 Price includes VAT (France)
Hardcover Book EUR 158.24 Price includes VAT (France)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Personalized Medicine Through Artificial Intelligence: A Public Health Perspective
Chapter © 2023
Big Data and Machine Learning in Healthcare: Concepts, Technologies, and Opportunities
Chapter © 2023
Artificial Intelligence-Based Healthcare Industry 4.0 for Disease Detection Using Machine Learning Techniques
Chapter © 2023
Notes
References
- A.M. Townsend, Smart cities: big data, civic hackers, and the Quest for a New Utopia (W.W. Norton & Company, New York, 2013) Google Scholar
- M. Bermudez-Edo, P. Barnaghi, K. Moessner, Analysing real world data streams with spatio-temporal correlations: entropy vs. pearson correlation. Automation in Construction88, 87–100 (2018) ArticleGoogle Scholar
- Q. Le-Dang, T. Le-Ngog, Internet of Things (IoT) Infrastructures for Smart Cities, in Handbook of Smart Cities: Software Services and Cyber Infrastructure, (Cham, Springer, 2018), pp. 1–30 Google Scholar
- P. Anatharam, P. Barnaghi, K. Thirunarayan, A. Sheth, Extracting city traffic events from social streams. ACM Trans. Intell. Syst. Technol. 6(4), 43.:1-43:27, (2015) Google Scholar
- A.K. Kar, S.Z. Mustafa, M.P. Gupta, P.V. Ilavarasan, Y.K. Dwivedi, Understanding Smart Cities: Inputs for Research and Practice, in Advances in Smart Cities: Smarter People, Governence, and Solutions, (CRC Press, Boca Ralton, 2017), p. 1 ChapterGoogle Scholar
- A. Yassine, S. Singh, A. Alamri, Mining human activity patterns from smart home big data for health care applications. IEEE Access 5, 13131–13141 (2017) ArticleGoogle Scholar
- B. Liu, K. He, G. Zhi, The impact of big data and artificial intelligence on the future medical model. Med. Philos. 39(22), 1–4. (in Chinese), (2018) Google Scholar
- S. Tian, W. Yang, J.M.L. Grange, P. Wang, W. Huang, Z. Ye, Smart healthcare: making medical care more intelligent. Glob. Health J. 3, 62–65 (2019) ArticleGoogle Scholar
- K. Kasikumar, M.M. Najumuddeen, R. Suresh, Applications of data mining techniques in healthcare and prediction of heart attacks. Int. J. Data Min. Tech. Appl. 7, 172–176 (2018) Google Scholar
- G.R. Pooja, M.B. Trinath, K. Vasanthi, K.S. Ramireddy, R.K. Tenali, Smart E-health prediction system using data mining. Int. J. Innov. Technol. Explor. Eng 8(6), 787–791 (2019) Google Scholar
- D.K. Singh, M. Ashraf, An experimental approach for prediction of disease in smart health system using data mining technique. Int. J. Adv. Sci. Technol. 27, 112–119 (2019). Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/102Google Scholar
- B. Kantarci, K.G. Carr, C.D. Pearsall, SONATA: Social Network Assisted Trustworthiness Assurance in Smart City Crowdsensing, in The Internet of Things: Breakthroughs in Research and Practice, (Hershey, IGI Global, 2017), pp. 278–299 ChapterGoogle Scholar
- J.A. Rodriguez, F.J. Fernadez, P. Arboleya, Study of the architecture of a smart city. Proceedings 2, 1–5 (2018) Google Scholar
- P. Lombardi, S. Giordano, Evaluating the Smart and Sustainable Built Environment in Urban Planning, in Handbook of Research on Social, Economic, and Environmental Sustainability in the Development of Smart Cities, (Hershey, IGI Global, 2015), pp. 44–59 ChapterGoogle Scholar
- H. Habibzadeh, A. Boggio-Dandry, Z. Qin, T. Soyata, B. Kantarci, H. Mouftah, Soft sensing in smart cities: handling 3Vs using recommender systems, machine intelligence, and data analytics. IEEE Commun. Mag. 56, 78–86 (2018) ArticleGoogle Scholar
- A. Souza, M. Figueredo, N. Cacho, D. Araujo, C.A. Prolo, Using big data and real-time analytics to support smart city initiatives. IFAC-Papers Online 49(30), 257–262 (2016) ArticleGoogle Scholar
- P. Koukaras, C. Tjortjis, D. Roussidis, Social media types: introducing a data driven taxonomy. Computing 102(1), 295–340 (2020) ArticleGoogle Scholar
- A. Mystakidis, C. Tjortjis, Big Data Mining for Smart Cities: Predicting Traffic Congestion using Classification, in The 11th International Conference on Information, Intelligence, Systems and Applications, Piraeus, 2020 Google Scholar
- S. Mohanty, U. Choppali, E. Kougianos, Everything you wanted to know about smart cities: the Internet of Things is the backbone. IEEE Consum. Electron. Mag. 5(3), 60–70 (2016) ArticleGoogle Scholar
- M.V. Moreno, F. Terroso-Saenz, A. Gonzalez-Vidal, M. Vldez-Vela, A. Skarmeta, M.A. Zamora, V. Chang, Applicability of big data techniques to smart cities deployments. IEEE Trans. Ind. Inf. 13(2), 800–809 (April 2017) ArticleGoogle Scholar
- J. Massana, C. Pous, L. Burgas, J. Melendez, J. Colomer, Identifying services for short-term load forecasting using data driven models in a Smart City platform. Sustain. Cities Soc. 28, 108–117 (2017) ArticleGoogle Scholar
- L. DeRen, C. JianJun, Y. Yuan, Big data in smart cities. Sci. China Inf. Sci. 58(12) (2015) Google Scholar
- D.J. Cook, G. Duncan, G. Sprint, R. Fritz, Using smart city technology to make healthcare smarter. Proc. IEEE 106, 708–722 (April 2018) ArticleGoogle Scholar
- K. Joo-Chang, C. Kyungyong, Depression index service using knowledge based crowdsourcing in smart health. Wirel. Pers. Commun. 93, 255–268 (March 2017) ArticleGoogle Scholar
- A. Copie, V.I. Munteanu, B. Manate, T.-F. Fortis, An Internet of Things Governance Architecture with Applications in Healthcare, in The Internet of Things: Breakthroughs in Research and Practice, (Hershey, IGI Global, 2017), pp. 112–136 ChapterGoogle Scholar
- A.A. Obinikpo, B. Kantarci, Big sensed data meets deep learning for smarter health care in smart cities. J. Sens. Actuator Netw. 6, 1–22 (2017) ArticleGoogle Scholar
- J. Dhar, A. Ranganathan, Machine learning capabilities in medical diagnosis applications: computational results for hepatitis disease. Int. J. Biomed. Eng. Technol. 17, 330–340 (2015) ArticleGoogle Scholar
- K. Polat, S. Gunes, Principles component analysis, fuzzy weighting pre-processing and artificial immune recognition system based diagnostic system for diagnosis of lung cancer, in Expert Systems with Applications, vol. 34, 1st edn., (2008), pp. 214–221 Google Scholar
- A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau, S. Thrun, Dermatologist-level classification of skin cancer with deep neural networks. Nature542, 115–118 (2017) ArticleGoogle Scholar
- S. Wang, R. Summers, Mechine Learning and Radiology. Med. Image Anal. 16(5), 933–951 (2012) ArticleGoogle Scholar
- S.P. Somashekhar, M.-J. Sepúlveda, S. Puglielli, A.E.H. Shortliffe, C. Kumar, A. Rauthan, N. Kumar, P. Patil, K. Rhee, Y. Ramya, Watson for oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board, in Annals of Oncology, vol. 29, 2nd edn., (2018), pp. 418–423 Google Scholar
- K. Kincade, Data mining: digging for healthcare gold. Insur. Technol. 23(2), IM2–IM7 (1998) Google Scholar
- A. Milley, Healthcare and data mining. Health Manag. Technol. 21(8), 44–47 (2000) Google Scholar
- J. Andreu-Perez, D. Leff, H. Ip, G. Yang, From wearable sensors to smart Implants—toward pervasive and personalized healthcare. IEEE Trans. Biomed. Eng. 62, 2750–2762 (2015) ArticleGoogle Scholar
- M. Chan, E. Campo, D. Estève, J.-Y. Fourniols, Smart homes—current features and future perspectives. Maturitas 64(2), 90–97 (2009) ArticleGoogle Scholar
- L. Liu, E. Strouli, I. Nikolaidis, A. Miguel-Cruz, A.R. Rincon, Smart homes and home health monitoring technologies for older adults: a systematic review. Int. J. Med. Inf. 91, 44–59 (July 2016) ArticleGoogle Scholar
- J. Zhang, Y. Li, L. Cao, Y. Zhang, Research on the construction of smart hospitals at home and abroad. Chin. Hos. Manag, 64–66 (2018) Google Scholar
- K. Li, J. Wang, T. Li, F. Dou, K. He, Application of internet of things in supplies logistics of intelligent hospital. Chin. Med. Equip., 172–176 (2018) Google Scholar
- H. Demirkan, A Smart Healthcare Systems Framework, IT Prof. no. 5, pp. 38–45, Sept–Oct 2013 Google Scholar
- Q. Chen, Y. Lu, Construction, and application effect evaluation of integrated manage-ment platform of intelligent hospital based on big data analysis. Chin. Med. Herald., 161–164 (2018) Google Scholar
- P. Piazza, Health alerts to fight bioterror, Secur. Manag. p. 40, 2002. Google Scholar
- J. Redfern, Smart health and innovation: facilitating health-related behaviour change. Proc. Nutr. Soc., 328–332 (2017) Google Scholar
- M. Ridinger, American healthways uses SAS to improve patient care, DM Rev. no. 12, p. 139, 2002. Google Scholar
- T.S. Brisimi, T. Xu, T. Wang, W. Dai, W.G. Adams, I.C. Paschalidis, Predicting chronic disease hospitalizations from electronic health records: an interpretable classification approach. Proc. IEEE 106(4), 690–707 (2018) ArticleGoogle Scholar
- S. Zhang, C. Tjortjis, X. Zeng, H. Qiao, B. Iain, J. Keane, Comparing data mining methods with logistic regression in childhood obesity prediction. Inf. Syst. Front. J. Springer 11(4), 449–460 (2009) ArticleGoogle Scholar
- C. Tjortjis, M. Saraee, B. Theodoulidis, J. Keane, Using T3, an improved decision tree classifier, for mining stroke related medical data. Method Inf Med Schattauer GmbH 46(5), 523–529 (2007) ArticleGoogle Scholar
- H. Banaee, M.U. Ahmed, A. Loutfi, Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors 13, 17472–17500 (2013) ArticleGoogle Scholar
- T. Nef, P. Urwuler, M. Buchler, I. Tarnanas, R. Stucki, D. Cazzoli, R. Muri, U. Mosimann, Evaluation of three state-of-the-art classifiers for recognition of activities of daily living from smart home ambient data. Sensors 15, 11725–11740 (2015) ArticleGoogle Scholar
- B. Lin, Y. Huangfu, N. Lima, B. Lobson, M. Kirk, P. O'Keeffe, S. Pressley, V. Walden, B. Lamb, D. Cook, Analyzing the relationship between human behavior and indoor air quality. J. Sens. Actuator Netw. 6, 1–18 (2017) ArticleGoogle Scholar
- M. Islam, M. Hasan, X. Wang, H. Germack, M. Noor-E-Alam, A systematic review on healthcare analytics: application and theoretical perspective of data mining. Healthcare 6(2), 54 (2018) ArticleGoogle Scholar
- N. Jothi, N.A. Rashid, W. Husain, Data Mining in Healthcare – A Review, in The Third Information Systems International Conference, 2015. Google Scholar
- M. Durairaj, V. Ranjani, Data Mining Applications In Healthcare Sector: A Study. Int. J. Sci. Technol. Res. 2(10), 29–35 (2013) Google Scholar
- V. Tatsis, C. Tjortjis, P. Tzirakis, Evaluating data mining algorithms using molecular dynamics trajectories. Int. J. Data Min. Bioinf. Indersci. 8(2), 169–187 (2013) ArticleGoogle Scholar
- P. Ahmad, S. Qamar, S.Q.A. Rizvi, Techniques of Data Mining In Healthcare: A Review. Int. J. Comp. Appl. 120(15), 38–50 (2015) Google Scholar
- H. Chung, P. Gray, Data mining. J. Manag. Inf. Syst. 16(1), 11–16 (1999) ArticleGoogle Scholar
- M. Aggarwal, Medium.com, 7 January 2018. [Online]. Available: https://medium.com/@thecodingcookie/cross-industry-process-for-data-mining-286c407132d0
- I. Parvathi, S. Rautaray, Survey on data mining techniques for the diagnosis of diseases in medical domain. Int. J. Comp. Sci. Inf. Technol. 5(1), 838–846 (2014) Google Scholar
- R. Martinez-Espana, A. Bueno-Crespo, I. Timon, J. Soto, A. Munoz, J.M. Cecilia, Air-pollution prediction in smart cities through machine learning methods: a case study in Murcia, Spain. J. Univer. Comp. Sci. 24(3), 261–276 (2018) Google Scholar
- P. Tzirakis and C. Tjortjis, "T3C: Improving a Decision Tree Classification Algorithm’s Interval Splits on Continuous Attributes," Advances in Data Analysis and Classification, Springer, vol. 11, no. 2, pp. 353-370, 2017. MATHGoogle Scholar
- S. Mohapatra, P.K. Patra, S. Mohanty, B. Pati, Smart Health Care System using Data Mining, in International Conference on Information Technology, 2018. Google Scholar
- D. Tomar, S. Agarwal, A survey on data mining approaches for Healthcare. Allahabad Int. J. Biosci. Biotechnol. 5(5), 241–266 (2013) Google Scholar
- Y. Kanellopoulos, P. Antonellis, C. Tjortjis, C. Makris, N. Tsirakis, k-Attractors: a partitional clustering algorithm for numeric data analysis. Appl. Artif. Intell. Taylor Francis 25(2), 97–115 (2011) ArticleGoogle Scholar
- A. Kelati, J. Plosila, H. Tenhunen, Smart Meter Load Profiling for e-Health Monitoring System, in 7th International Conference on Smart Energy Grid Engineering, 2019. Google Scholar
- R. Agrawal and R. Srikant, "Apriori algorithm". 1994. Google Scholar
- S.M. Ghafari, and C. Tjortjis, Association Rules Mining by improving the Imperialism Competitive Algorithm (ARMICA), in IFIP AICT Proceeding of 12th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2016). Springer,, 2016. Google Scholar
- S. Yakhchi, S. M. Ghafari, C. Tjortjis, M. Fazeli, ARMICA-Improved: A New Approach for Association Rule Mining, in Proceeding of 10th International Conference on Knowledge Science, Engineering and Management (KSEM 17), Springer LNAI, vol. 10412, pp. 296–306, 2017. Google Scholar
- J. Han, H. Pei, Y. Yin, Mining Frequent Patterns without Candidate Generation, in Proceeding of Conference on the Management of Data (SIGMOD’00, Dallas, TX), (ACM Press, New York, 2000) Google Scholar
- S.M. Ghafari, C. Tjortjis, A survey on association rules mining using Heuristics. WIREs Data Min. Knowl. Discov. 9(4) (2019) Google Scholar
- Y. Ji, H. Ying, J. Tran, P. Dews, A. Mansour, M.R. Massanari, Mining Infrequent Causal Associations in Electronic Health Databases, in 2011 IEEE 11th Int’l Conf. on Data Mining Workshops, 2011. Google Scholar
- A. Asuncion, D. Newman, UCI Machine Learning Repository, 2007. [Online]. Available: https://archive.ics.uci.edu/ml/datasets.php
- H. Ian, E. Frank, A.H. Mark, J.P. Christopher, Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. (Morgan Kaufmann, San Francisco, 2011) Google Scholar
- R.L. Thorndike, Who belongs in the family? Psychometrika18, 267–276 (1953) ArticleGoogle Scholar
Author information
Authors and Affiliations
- The Data Mining and Analytics research group, School of Science and Technology, International Hellenic University, Thermi, Greece Theodora Chatzinikolaou, Eleni Vogiatzi, Anestis Kousis & Christos Tjortjis
- Theodora Chatzinikolaou
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
Corresponding author
Editor information
Editors and Affiliations
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India Shalli Rani
- Intel Corporation, Folsom, CA, USA Vyasa Sai
- Department of ECE, KPR Institute of Engineering and Technology, Coimbatore, India R. Maheswar
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Chatzinikolaou, T., Vogiatzi, E., Kousis, A., Tjortjis, C. (2022). Smart Healthcare Support Using Data Mining and Machine Learning. In: Rani, S., Sai, V., Maheswar, R. (eds) IoT and WSN based Smart Cities: A Machine Learning Perspective. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-84182-9_3
Download citation
- DOI : https://doi.org/10.1007/978-3-030-84182-9_3
- Published : 31 May 2022
- Publisher Name : Springer, Cham
- Print ISBN : 978-3-030-84181-2
- Online ISBN : 978-3-030-84182-9
- eBook Packages : EngineeringEngineering (R0)
Share this chapter
Anyone you share the following link with will be able to read this content:
Get shareable link
Sorry, a shareable link is not currently available for this article.
Copy to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative