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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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
Devi MR, Shyla JM (2016) Analysis of various data mining techniques to predict diabetes mellitus. Int J Appl Eng Res 11(1):727–730
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
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
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
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
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
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
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
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
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
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
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
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
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
Vijayarani S, Dhayanand S (2015) Data mining classification algorithms for kidney diseases prediction. International Journal on Cybernetics & Informatics 4(4):13–25
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
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-7327-8