A healthcare monitoring system using random forest and internet of things (IoT)

Abstract

The Internet of Things (IoT) enabled various types of applications in the field of information technology, smart and connected health care is notably a crucial one is one of them. Our physical and mental health information can be used to bring about a positive transformation change in the health care landscape using networked sensors. It makes it possible for monitoring to come to the people who don’t have ready access to effective health monitoring system. The captured data can then be analyzed using various machine learning algorithms and then shared through wireless connectivity with medical professionals who can make appropriate recommendations. These scenarios already exist, but we intend to enhance it by analyzing the past data for predicting future problems using prescriptive analytics. It will allow us to move from reactive to visionary approach by rapidly spotting trends and making recommendations on behalf of the actual medical service provider. In this paper, the authors have applied different machine learning techniques and considered public datasets of health care stored in the cloud to build a system, which allows for real time and remote health monitoring built on IoT infrastructure and associated with cloud computing. The system will be allowed to drive recommendations based on the historic and empirical data lying on the cloud. The authors have proposed a framework to uncover knowledge in a database, bringing light to disguise patterns which can help in credible decision making. This paper has evaluated prediction systems for diseases such as heart diseases, breast cancer, diabetes, spect_heart, thyroid, dermatology, liver disorders and surgical data using a number of input attributes related to that particular disease. Experimental results are conducted using a few machine learning algorithms considered in this paper like K-NN, Support Vector Machine, Decision Trees, Random Forest, and MLP.

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References

  1. 1.

    Costa K, Ribeiro P, Carmargo A, Rossi V, Martins H, Neves M, Fabris R, Imaisumi R, Papa JP (2013) Comparison of the techniques decision tee and MLP for data mining in SPAMs detection in computer networks. Proceedings of the 3rd international conference on innovative computing technology, pp 344–348

  2. 2.

    Devi MR, Shyla JM (2016) Analysis of various data mining techniques to predict diabetes mellitus. Int J Appl Eng Res 11(1):727–730

    Google Scholar 

  3. 3.

    Diaz-Uriarte R, Alverez-de-Andres S (2006) Gene selection and classification of microarray data using random forest. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-7-3

  4. 4.

    Forkan ARM, Khalil I, Atiquzzaman M (2017) ViSiBiD: a learning model for early discovery and real time prediction of severe clinical events using vital signs as big data. Comput Netw 113:244–257

    Article  Google Scholar 

  5. 5.

    Hameed RT, Mohamad OA, Hamid OT, Tapus N (2015) Design of e-healthcare management system Basedon cloud and service oriented architecture. Proceedings of the 5th IEEE international conference on E-health and bioengineering (EHB), pp 1–4

  6. 6.

    Hsu JL, Hung PC, Lin HY, Hsieh CH (2015) Applying under-sampling techniques and cost-sensitive learning methods on risk assessment of breast cancer. J Med Syst 39(4). https://doi.org/10.1007/s10916-015-0210-x

  7. 7.

    Jahangir M, Afzal H, Ahmed M, Khurshid K, Nawaz R (2017) An expert system for diabetes prediction using auto tuned multi-layer perceptron. Proceedings of the intelligent system conference, pp 722–728

  8. 8.

    Osman AH, Aljahdali HM (2017) Diabetes disease diagnosis method based on feature extraction using k-svm. Int J Adv Comput Sci Appl 8(1):236–244

    Google Scholar 

  9. 9.

    Parekh M, Saleena B (2015) Designing a cloud based framework for healthcare system and applying clustering techniques for region wise diagnosis. 2nd international symposium on big data and cloud computing (ISBCC’15), 50:537–542

  10. 10.

    Tao D, Wen Y, Hong R (2016) Multicolumn bidirectional long short-term memory for mobile devices-based human activity recognition. IEEE Internet Things J 3(6):1124–1134

    Article  Google Scholar 

  11. 11.

    Tao D, Cheng J, Gao X, Li X, Deng C (2017) Robust sparse coding for Mobile image labeling on the cloud. IEEE Trans Circuits Syst Video Technol 27(1):62–72

    Article  Google Scholar 

  12. 12.

    Tomar D, Agarwal S (2013) A survey on data mining approaches for healthcare. International Journal of Bio-Science and Bio-Technology 5(5):241–266

    Article  Google Scholar 

  13. 13.

    Turanoglu-Bekar E, Ulutagay G, Kantarc-Savas S (2016) Classification of thyroid disease by using data mining models: a comparison of decision tree algorithm. Oxford Journal of Intelligent Decision and Data Science 2:13–28

    Article  Google Scholar 

  14. 14.

    Verikas A, Gelzinis A, Bacauskiene M (2011) Mining data with random forest: a survey and results of new tests. Pattern Recogn 44(2):330–349

    Article  Google Scholar 

  15. 15.

    Verma L, Srivastava S, Negi PC (2016) A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. J Med Syst 40(7):178

    Article  Google Scholar 

  16. 16.

    Vijayarani S, Dhayanand S (2015) Data mining classification algorithms for kidney diseases prediction. International Journal on Cybernetics & Informatics 4(4):13–25

    Article  Google Scholar 

  17. 17.

    Zhang L, Zhou W, Wang B, Zhang Z, Li F (2018) Applying 1-norm svm with squared loss to gene selection for cancer classification. Appl Intell 48(7):1878–1890

    Article  Google Scholar 

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Correspondence to Munish Kumar.

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Kaur, P., Kumar, R. & Kumar, M. A healthcare monitoring system using random forest and internet of things (IoT). Multimed Tools Appl 78, 19905–19916 (2019). https://doi.org/10.1007/s11042-019-7327-8

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Keywords

  • Internet of things
  • Data mining
  • Machine learning
  • Healthcare