In Healthcare, prevention and cure have seen diverse advancement in technological schema. Chronic care and prevention care both stand on equal level with the same advancement in technology. We propose PREVASIVE method towards healthcare diagnosis. The word ‘EVASIVE’ means ‘something which is intended to come’. The prosing word ‘PERVASIVE’ is to mean prevention against the one which is intended to come. In medical history, technology contributes towards diagnosis through machine learning algorithms. Machine learning algorithms are also applied for prediction towards prevention of various diseases and this in course help for cure for specific disease. We propose diagnosis of health through inheritance traits and surroundings the person inhibits from. For knowledge, inheritance traits, history of the person is collected as PHR (Personal Health Record). The GPS (Global Positioning System) module is used to see where the person inhibits. Location and movement of person is taken into consideration to know if the region has the history of any specific diseases’ and GPS module applied with appropriate machine learning algorithms can help us determine diagnosis for diseases which are intended to come towards the specific person.

Machine learning algorithm,PHR,Healthcare,dengue,swine flu,heart diease

[1] A. Gavhane, G. Kokkula, I. Pandya and P. K. Devadkar, "Prediction of Heart Disease Using Machine Learning," 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, 2018, pp. 1275-1278.
[2] B. A. Thakkar, M. I. Hasan and M. A. Desai, "Health Care Decision Support System for Swine Flu Prediction Using Naïve Bayes Classifier," 2010 International Conference on Advances in Recent Technologies in Communication and Computing, Kottayam, 2010, pp. 101-105.
[3] V. S. H. Rao and M. N. Kumar, "A New Intelligence-Based Approach for Computer-Aided Diagnosis of Dengue Fever," in IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 1, pp. 112-118, Jan. 2012.
doi: 10.1109/TITB.2011.2171978
[4] J. Park, K. Kim and O. Kwon, "Comparison of machine learning algorithms to predict psychological wellness indices for ubiquitous healthcare system design," Proceedings of the 2014 International Conference on Innovative Design and Manufacturing (ICIDM), Montreal, QC, 2014, pp. 263-269.
[5] S. Örenç, M. K. Uçar, M. R. Bozkurt and C. Bilgin, "A new approach for treatment of chronic obstructive pulmonary disease," 2017 Medical Technologies National Congress (TIPTEKNO), Trabzon, 2017, pp. 1-4.
doi: 10.1109/TIPTEKNO.2017.8238034
[6] P. Naraei, A. Abhari and A. Sadeghian, "Application of multilayer perceptron neural networks and support vector machines in classification of healthcare data," 2016 Future Technologies Conference (FTC), San Francisco, CA, 2016, pp. 848-852. A. Mardin, T. Anwar, B. Anwer, “Image Compression: Combination of Discrete Transformation and Matrix Reduction”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.1-6, 2017.
[7] M. Moein, Mohammad Davarpanah, M. A. Montazeri and M. Ataei, "Classifying ear disorders using support vector machines," 2010 Second International Conference on Computational Intelligence and Natural Computing, Wuhan, 2010, pp. 321-324.
[8] P. Manivannan and P. I. Devi, "Dengue fever prediction using K-means clustering algorithm," 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Srivilliputhur, 2017, pp. 1-5. C.T. Lee, A. Girgensohn, J. Zhang, “Browsers to support awareness and Social Interaction,” Computer Graphics and Applications, Journal of IEEE Access , Vol.24, Issue.10, pp.66-75, 2012. doi: 10.1109/MCG.2004.24
[9] N. Mago, S. Srivastava, R. D. Shirwaikar, U. D. Acharya, L. E. S. Lewis and M. Shivakumar, "Prediction of Apnea of Prematurity in neonates using Support Vector Machines and Random Forests," 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, 2016, pp. 693-697. S. Tamilarasan, P.K. Sharma, “A Survey on Dynamic Resource Allocation in MIMO Heterogeneous Cognitive Radio Networks based on Priority Scheduling”, International Journal of Computer Sciences and Engineering, Vol.5, No.1, pp.53-59, 2017.
[10] L. Jena and R. Swain, "Work-in-Progress: Chronic Disease Risk Prediction Using Distributed Machine Learning Classifiers," 2017 International Conference on Information Technology (ICIT), Bhubaneswar, 2017, pp. 170-173.
doi: 10.1109/ICIT.2017.46
[11] C. Hung, W. Chen, P. Lai, C. Lin and C. Lee, "Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database," 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, 2017, pp. 3110-3113.
[12] T. Christensen, A. Frandsen, S. Glazier, J. Humpherys and D. Kartchner, "Machine Learning Methods for Disease Prediction with Claims Data," 2018 IEEE International Conference on Healthcare Informatics (ICHI), New York, NY, 2018, pp. 467-4674.
[13] M. Chen, Y. Hao, K. Hwang, L. Wang and L. Wang, "Disease Prediction by Machine Learning Over Big Data From Healthcare Communities," in IEEE Access, vol. 5, pp. 8869-8879, 2017.
[14] B. Chakraborty, S. I. Srinivas, P. Sood, V. Nabhi and D. Ghosh, "Case Based Reasoning methodology for diagnosis of Swine Flu," 2011 IEEE GCC Conference and Exhibition (GCC), Dubai, 2011, pp. 132-135.
[15] S. Athmaja, M. Hanumanthappa and V. Kavitha, "A survey of machine learning algorithms for big data analytics," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, 2017, pp. 1-4.
[16] O. Rajabi Shishvan, D. Zois and T. Soyata, "Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey," in IEEE Access, vol. 6, pp. 46419-46494, 2018.
[17] M. Chen, Y. Hao, K. Hwang, L. Wang and L. Wang, "Disease Prediction by Machine Learning Over Big Data From Healthcare Communities," in IEEE Access, vol. 5, pp. 8869-8879, 2017.
[18] K. Shailaja, B. Seetharamulu and M. A. Jabbar, "Machine Learning in Healthcare: A Review," 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, 2018, pp. 910-914.
[19] K. G. Dinesh, K. Arumugaraj, K. D. Santhosh and V. Mareeswari, "Prediction of Cardiovascular Disease Using Machine Learning Algorithms," 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), Coimbatore, India, 2018, pp. 1-7.
[20] P. Siriyasatien, S. Chadsuthi, K. Jampachaisri and K. Kesorn, "Dengue Epidemics Prediction: A Survey of the State-of-the-Art Based on Data Science Processes," in IEEE Access, vol. 6, pp. 53757-53795, 2018.