Performance Analysis of Student Healthcare Dataset using Classification Algorithm

Muhammad Saqib Javed


Nowadays health is considered as a backbone in terms of performance based on Internet of things (IoT devices), which turned out to be important in diagnosing health level of person with the type of disease a person is suffering with plus its severity level. Basically, IoT sensors operate on medical devices produce large volume of dynamic data. The fluctuation in health data, which forced to use data mining tools and techniques for extracting useful data. Therefore, for applying data mining techniques, heterogeneous data needs to be preprocessed. Therefore, by refining the collection of data, health parametric data mining yields better results with associated benefits. The decision tree is employed in order to consolidate the health attributes of the students to decide the level of health scale. This could leads to evaluate the level of performance of the student in class. After mining the student’s health data it is passed to K-Fold cross validation check, so that to determine the accuracy, error rate, precision and recall. This is considered as enhanced diagnosis method as fixed patterns for decision tree makes precise decisions. Considering a case study of students health prediction based on certain attributes and the level of attributes (Normality to study) diagnostic as pattern based using K-NN and decision tree algorithm, which are tested on trained dataset using WEKA tool. At the end, the comparison of different algorithms will be reflected to generalize the introduction of optimized classification algorithm.


Support Vector Machine; K- nearest neighbor classifier;IoT;SHCMF; W-NNC

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“[1] Shirwalkar, N., Gursalkar, S., Tak, T., & Kalshetti, A. (2018). Human Heart Disease Prediction System Using Data Mining Techniques.

Tien J, Goldschmidt-clermont P (2009). Healthcare: a complex service system. J Syst Sci Syst Eng, 18(3):257-82.

KARAMI, M., & SHAHMIRZADI, A. H. (2018). Applying Agent-based Technologies in Complex Healthcare Environment. Iranian Journal of Public Health, 47(3), 458-459.

K.Srinivas B.Kavihta Rani Dr. A.Govrdhan, “Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks”, (IJCSE) International Journal on Computer Science and Engineering, 2010.

Divya Tomar and Sonali Agarwal(2013), “A survey on Data Mining approaches for Healthcare”. International Journal of Bio-Science and Bio-Technology Vol.5, No.5 (2013), pp. 241-266

IIIhoi, Patricia Alafaireet, et at (2012), “Data Mining in Healthcare and Biomedicine: A Survey of the Literature”. Journal of medical sciences Volume 36, Issue 4, pp 2431-2448

Pal, D., Jain, A., Saxena, A., & Agarwal, V. (2016). Comparing Various Classifier Techniques for Efficient Mining of Data. In Proceedings of the International Congress on Information and Communication Technology (pp. 191-202). Springer, Singapore.

Keshan, N., Bichindaritz, I., Parimi, P. V., & Phoha, V. V. (2017, August). Temporal Analysis of Stress Classification Using QRS Complex of ECG Signals. In International Symposium on Sensor Networks, Systems and Security (pp. 35-44). Springer, Cham.

Monali Dey and Siddharth Swarup Rautaray, “Study and Analysis of Data mining Algorithms for Healthcare Decision Support System”. International Journal of Computer Science and Information Technologies, Vol. 5 (1), 2014, pp 470-477

Jayanthi Ranjan. “Applications of data mining techniques in pharmaceutical industry” . Journal of Theoretical and Applied Information Technology (20052007).,pp(61-67)

Kumari, M., & Godara, S. (2011). Review of data mining classification models in cardiovascular disease diagnosis. International Journal of Computer Science and Technology, 2(2), 304-305.

Sareen, S., Sood, S. K., & Gupta, S. K. (2016). IoT-based cloud framework to control Ebola virus outbreak. Journal of Ambient Intelligence and Humanized Computing, 1-18.

Verma, P., Sood, S. K., & Kalra, S. (2018). Cloud-centric IoT based student healthcare monitoring framework. Journal of Ambient Intelligence and Humanized Computing, 9(5), 1293-1309.

Polat, Kemal and Salih Gunes, “An expert system approach based on principal component analysis and adaptive neurofuzzy inference system to diagnosis of diabetes disease,” Expert system with Applications, pp. 702-710, Elsevier, 2007.

D. Deng and N. Kasabov, “ On-line pattern analysis by evolving self- organizing maps”, In Proceedings of the fifth biannual conference on artificial neural networks and expert systems (ANNES), 2001, pp. 46- 51.

Yue, et al. “An Intelligent Diagnosis to Type 2 Diabetes Based on QPSO Algorithm and WLSSVM,” International Symposium on Intelligent Information Technology Application Workshops, IEEE Computer Society, 2008.

Smith, J.W., J. E. Everhart, et al.- “Using the ADAP learning algorithm to forecast the onset of diabetes mellitus”, Proceedings of the Symposium on Computer Applications and Medical Care (Washington, DC). R.A. Greenes. Los Angeles, CA, IEEE Computer Society Press, 1988, pp. 261-265.

S.Sahan, K.Polat, H. Kodaz, and S. Gunes, “The medical applications of attribute weighted artificial immune system (awais): Diagnosis of heart and diabetes diseases”, in ICARIS, 2005, p. 456-468

Al-Shehri, H., Al-Qarni, A., Al-Saati, L., Batoaq, A., Badukhen, H., Alrashed, S., ... & Olatunji, S. O. (2017, April). Student performance prediction using Support Vector Machine and K-Nearest Neighbor. In Electrical and Computer Engineering (CCECE), 2017 IEEE 30th Canadian Conference on (pp. 1-4). IEEE”.


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