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Intelligent Heart Disease Prediction on Physical and Mental Parameters: A ML Based IoT and Big Data Application and Analysis

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Book cover Machine Learning with Health Care Perspective

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 13))

Abstract

Nearly 17.5 million deaths from cardiovascular disease occur worldwide. Currently, India has more than 30 million heart patients. People’s unconscious attitudes towards health are likely to lead to a variety of illnesses and can be life threatening. In the healthcare industry, large amounts of data are frequently generated. However, it is often not used effectively. The data indicates that the generated image, sound, text, or file has some hidden patterns and their relationships. Tools used to extract knowledge from these databases for clinical diagnosis of disease or other purposes are less common. Of course, if you can create a mechanism or system that can communicate your mind to people and alert you based on your medical history, it will help. Current experimental studies use machine learning (ML) algorithms to predict risk factors for a person’s heart disease, depending on several characteristics of the medical history. Use input features such as gender, cholesterol, blood pressure, TTH, and stress to predict the patient’s risk of heart disease. Data mining (DM) techniques such as Naive Bayes, decision trees, support vector machines, and logistic regression are analyzed in the heart disease database. The accuracy of various algorithms is measured and the algorithms were compared. The result of this experimental analysis is a 0 or 1 result that poses no danger or danger to the individual. Django is used to run a website.

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References

  1. M. Ashina, L.H. Lassen, L. Bendtsen, R. Jensen, J. Olesen, Effect of inhibition of nitric oxide synthase on chronic tension-type headache: a randomized crossover trial. Lancet 353(9149), 287–289 (1999)

    Article  Google Scholar 

  2. S. Chauhan, R. Rastogi, D.K. Chaturvedi, N. Arora, P. Trivedi, Framework for use of machine intelligence on clinical psychology to study the effects of spiritual tools on human behavior and psychic challenges, in Proceedings of NSC-2017 (National System Conference), DEI, Agra, 1–3 December 2017

    Google Scholar 

  3. R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, V. Yadav, S. Chauhan, P. Sharma, SF-36 scores analysis for EMG and GSR therapy on audio, visual and audio visual modes for chronic TTH, in Proceedings of the ICCIDA-2018 on 27 and 28th October 2018. CCIS Series (Springer, Gandhi Institute for Technology, Khordha, Bhubaneswar, Odisha, India, 2018)

    Google Scholar 

  4. J.-B. Waldner, Nanoinformatique et intelligence ambiante, Inventer l’Ordinateur du XXIeme Siècle (Hermes Science, London, 2007), p. 254. ISBN 978-2-7462-1516-0

    Google Scholar 

  5. M. Gulati, R. Rastogi, D.K. Chaturvedi, P. Sharma, V. Yadav, S. Chauhan, M. Gupta, P. Singhal, Statistical resultant analysis of psychosomatic survey on various human personality indicators: statistical survey to map stress and mental health, Handbook of Research on Learning in the Age of Transhumanism (IGI Global, Hershey, PA, 2019), pp. 363–383 (chapter 22). https://doi.org/10.4018/978-1-5225-8431-5.ch022. ISSN: 2326-8905|EISSN: 2326-8913

  6. G. Bronfort et al., Non-invasive physical treatments for chronic/recurrent headache. Cochrane Database Syst. Rev. (3), CD001878 (2004). https://doi.org/10.1002/14651858.cd001878.pub2.pmid15266458

  7. A. Sharma, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, P. Trivedi, A. Singh, A. Singh, Intelligent analysis for personality detection on various indicators by clinical reliable psychological TTH and stress surveys, in Proceedings of CIPR 2019 at Indian Institute of Engineering Science and Technology, Shibpur on 19–20th January 2019. AISC Series (Springer, 2019a)

    Google Scholar 

  8. Commission of the European Communities, Internet of Things—An Action Plan for Europe, COM-278 (18 June 2009)

    Google Scholar 

  9. D.K. Chaturvedi, Human rights and consciousness, in International Seminar on Prominence of Human Rights in the Criminal Justice System (ISPUR 2012), Organized Ambedkar Chair, Dept. of Contemporary Social Studies & Law, Dr. B.R. Ambedkar University, Agra, 30–31 March 2012, p. 33

    Google Scholar 

  10. D.M. Biondi, Physical treatments for headache: a structured review. Headache 45(6), 738–746 (2005). https://doi.org/10.1111/j.1526-4610.2005.05141.x.pmid15953306

    Article  Google Scholar 

  11. D.K. Chaturvedi, M. Arya, Correlation between human performance and consciousness, in IEEE-International Conference on Human Computer Interaction, Saveetha School of Engineering, Saveetha University, Thandalam, Chennai, IN, India, 23–24 August 2013

    Google Scholar 

  12. E. Van der Zee, H. Scholten, Spatial dimensions of big data: application of geographical concepts and spatial technology to the Internet of Things. Stud. Comput. Intell. 23, 156–178 (2014)

    Google Scholar 

  13. D.K. Chaturvedi, R. Satsangi, The correlation between student performance and consciousness level, in International Conference on Advanced Computing and Communication Technologies (ICACCT™-2013), Asia Pacific Institute of Information Technology SD India, Panipat (Hariyana), Souvenir, 16 November 2013, pp. 66, 200–203

    Google Scholar 

  14. P. Sharma, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, V. Yadav, S. Chauhan, Analytical comparison of efficacy for electromyography and galvanic skin resistance biofeedback on audio-visual mode for chronic TTH on various attributes, in Proceedings of the ICCIDA-2018 on 27 and 28th October 2018. CCIS Series (Springer, Gandhi Institute for Technology, Khordha, Bhubaneswar, Odisha, India, 2018)

    Google Scholar 

  15. M.E. Lenaerts, Burden of tension-type headache. Curr. Pain Headache Rep. 45(2), 57–69 (2006)

    Google Scholar 

  16. R. Rastogi, D.K. Chaturvedi, N. Arora, P. Trivedi, V. Mishra, Swarm intelligent optimized method of development of noble life in the perspective of Indian scientific philosophy and psychology, in Proceedings of NSC-2017 (National System Conference), DEI, Agra, 1–3 December 2017

    Google Scholar 

  17. N. Bessis, C. Dobre (eds.), Big Data and Internet of Things: A Roadmap for Smart Environments (Springer, Cham, 2014), pp. 137–168. ISBN 9783319050294

    Google Scholar 

  18. A.P. Verhagen, L. Damen, M.Y. Berger, J. Passchier, B.W. Koes, Lack of benefit for prophylactic drugs of tension-type headache in adults: a systematic review. Fam. Pract. 4(3), 156–178 (2010)

    Google Scholar 

  19. D.K. Chaturvedi, Science, religion and spiritual quest, Edited book on Linkages between Social Service, Agriculture and Theology for the Future of Mankind (DEI Press, 2004), pp. 15–17

    Google Scholar 

  20. P. Vyas, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, P. Singh, Statistical analysis for effect of positive thinking on stress management and creative problem solving for adolescents, in Proceedings of the 12th INDIACom, 2018, pp. 245–251. ISSN 0973–7529 and ISBN 978-93-80544-14-4

    Google Scholar 

  21. Y. Nestoriuc, W. Rief, A. Martin, Meta-analysis of biofeedback for tension-type headache: efficacy, specificity, and treatment moderators. J. Consult. Clin. Psychol. 76(3), 379–396 (2008). https://doi.org/10.1037/0022-006x.76.3.379.pmid18540732

    Article  Google Scholar 

  22. J.-L. Gassée, Internet of Things: The “Basket of Remotes” Problem (12 January 2014)

    Google Scholar 

  23. D.K. Chaturvedi, S. Rajeev, The correlation between student performance and consciousness level. Int. J. Comput. Sci. Commun. Technol. 6(2), 936–939 (2014). ISSN: 0974-3375

    Google Scholar 

  24. R. Want, B.N. Schilit, S. Jenson, Enabling the Internet of Things (IEEE Computer Society, IEEE, 2015), pp. 28–35

    Google Scholar 

  25. D.K. Chatruvedi, Lajwanti, Correlation between energy distribution profile and level of consciousness. Shiakshk Parisamvad Int. J. Educ. SPIJJE 4(1), 1–9 (2014). ISSN: 2231-2323

    Google Scholar 

  26. M. Gulati, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, P. Singhal, Statistical resultant analysis of spiritual & psychosomatic stress survey on various human personality indicators, in International Conference Proceedings of ICCI 2018, 2018

    Google Scholar 

  27. Yaniv Chen, Advances in the pathophysiology of tension-type headache: From stress to central sensitization. Curr. Pain Headache Rep. 56(1), 43–49 (2009)

    Google Scholar 

  28. A. Agrawal, R. Rastogi, D.K. Chaturvedi, S. Sharma, A. Bansal, Audio visual EMG & GSR biofeedback analysis for effect of spiritual techniques on human behavior and psychic challenges, in Proceedings of the 12th INDIACom, 2018, pp. 252–258. ISSN 0973–7529 and ISBN 978-93-80544-14-4

    Google Scholar 

  29. S. Greengard, The Internet of Things (MIT Press, Cambridge, MA, 2015), p. 90. ISBN 9780262527736

    Google Scholar 

  30. D.K. Chaturvedi, M. Arya, A study of correlation between consciousness level and performance of worker. Ind. Eng. J. 6(8), 40–43 (2013). D.K. Chaturvedi, Lajwanti, Dayalbagh way of life for better worldliness. Quest J. J. Res. Hum. Soc. Sci. 3(5), 16–23 (2015). ISSN(Online): 2321-9467

    Google Scholar 

  31. W.M. Kang, Y.S. Moon, J.H. Park, An enhanced security framework for home appliances in smart home. Human-centric Comput. Inf. Sci. 7(6) (2017). https://doi.org/10.1186/s13673-017-0087-4

  32. L. Bendtsen, R. Jensen, Treating tension-type headache—an expert opinion. Expert Opin. Pharmacother. 12(7), 1099–1109 (2011)

    Article  Google Scholar 

  33. D.K. Chaturvedi, J.K. Arora, R. Bhardwaj, Effect of meditation on chakra energy and hemodynamic parameters. Int. J. Comput. Appl. 126(12), 52–59 (2015)

    Google Scholar 

  34. V. Yadav, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, M. Gupta, S. Chauhan, P. Sharma, Chronic TTH analysis by EMG & GSR biofeedback on various modes and various medical symptoms using IoT. Advances in ubiquitous sensing applications for healthcare. Big Data Analytics for Intelligent Healthcare Management (2019). ISBN: 9780128181461

    Google Scholar 

  35. Q.F. Hassan, Internet of Things A to Z: Technologies and Applications (Wiley, Hoboken, NJ, 2018), pp. 41–44. ISBN 9781119456759

    Google Scholar 

  36. D.M. Simpson, M. Hallett, E.J. Ashman, C.L. Comella, M.W. Green, G.S. Gronseth, M.J. Armstrong, D. Gloss, S. Potrebic, J. Jankovic, B.P. Karp, Headache and disease. Naumann 12(2), 23–39 (2016)

    Google Scholar 

  37. D.K. Chaturvedi, Relationship between chakra energy and consciousness. Biomed. J. Sci. Tech. Res. 15(3), 1–3 (2019), https://doi.org/10.26717/bjstr.2019.15.002705. ISSN: 2574-1241

  38. P. Singh, R. Rastogi, D.K. Chaturvedi, N. Arora, P. Trivedi, P. Vyas, Study on efficacy of electromyography and electroencephalography biofeedback with mindful meditation on mental health of youths, in Proceedings of the 12th INDIACom, 2018, pp. 84–89. ISSN 0973–7529 and ISBN 978-93-80544-14-4

    Google Scholar 

  39. L.J. Kricka, History of disruptions in laboratory medicine: what have we learned from predictions? Clin. Chem. Lab. Med. (2018). https://doi.org/10.1515/cclm-2018-0518 (inactive 2018-11-27). PMID: 29927745

  40. S. Derry, P.J. Wiffen, R.A. Moore, Aspirin for acute treatment of episodic tension-type headache in adults. Cochrane Database Syst. Rev. 12(6), 45–57 (2017)

    Google Scholar 

  41. Richa, D.K. Chaturvedi, S. Prakash, The consciousness in Mosquito. J. Mosquito Res. 6(34), 1–9 (2016). ISSN: 1927-646X

    Google Scholar 

  42. V. Singh, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, H. Sirohi, M. Singh, P. Verma, Which one is best: electromyography biofeedback efficacy analysis on audio, visual and audio-visual modes for chronic TTH on different characteristics, in Proceedings of ICCIIoT-2018, 14–15 December 2018 at NIT Agartala, Tripura (ELSEVIER-SSRN Digital Library, 2018). ISSN 1556-5068

    Google Scholar 

  43. R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, S. Chauhan, An optimized biofeedback therapy for chronic TTH between electromyography and galvanic skin resistance biofeedback on audio, visual and audio visual modes on various medical symptoms, in National Conference on 3rd MDNCPDR-2018 at DEI, Agra on 06–07 September 2018

    Google Scholar 

  44. H. Verma, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, H. Saini, K. Mehlyan, Y. Varshney, Statistical analysis of EMG and GSR therapy on visual mode and SF-36 scores for chronic TTH, in Proceedings of UPCON-2018 on 2–4 November 2018 MMMUT, Gorakhpur, UP, 2018

    Google Scholar 

  45. R. Walls, R. Hockberger, M. Gausche-Hill, Rosen’s emergency. Med Concepts Clin Pract 13(3), 37–47 (2017)

    Google Scholar 

  46. H. Saini, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, H. Verma, K. Mehlyan, Comparative efficacy analysis of electromyography and galvanic skin resistance biofeedback on audio mode for chronic TTH on various indicators, in Proceedings of ICCIIoT-2018, 14–15 December 2018 at NIT Agartala, Tripura (ELSEVIER-SSRN Digital Library, 2018). ISSN 1556-5068

    Google Scholar 

  47. Richa, D.K. Chaturvedi, S. Prakash, Role of electric and magnetic energy emission in intra and interspecies interaction in microbes. Am. J. Res. Commun. 4(12), 1–22 (2016). ISSN: 2325-4076

    Google Scholar 

  48. D.K. Chaturvedi, Lajwanti, T.H. Chu, H.P. Kohli, Energy distribution profile of human influences the level of consciousness, in Towards a Science of Consciousness, Arizona Conference Proceeding, Tucson, Arizona, 2012

    Google Scholar 

  49. J. Smith, Ferri’s Clinical Advisor (Elsevier, Philadelphia, 2019), p. 1348. ISBN 978-0-323-53042-2

    Google Scholar 

  50. V. Yadav, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, I. Bansal, Intelligent analysis for detection of complex human personality by clinical reliable psychological surveys on various indicators, in National Conference on 3rd MDNCPDR-2018 at DEI, Agra on 06–07 September 2018

    Google Scholar 

  51. P.M. Gadient, J. Smith, The neuralgias: diagnosis and management. Curr. Neurol. Neurosci. Rep. 14, 459 (2014)

    Article  Google Scholar 

  52. S. Palaniappan, R. Awang, Intelligent heart disease prediction system using data mining techniques, in IEEE/ACS International Conference on Computer Systems and Applications, Doha, 2008, pp. 108–115. https://doi.org/10.1109/aiccsa.2008.4493524

  53. M. Sultana, A. Haider, M.S. Uddin, Analysis of data mining techniques for heart disease prediction, in 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka, 2016, pp. 1–5. https://doi.org/10.1109/ceeict.2016.7873142

  54. V. Yadav, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, V. Yadav, P. Sharma, S. Chauhan, Statistical analysis of EMG & GSR biofeedback efficacy on different modes for chronic TTH on various indicators. Int. J. Adv. Intell. Paradig. 13(1), 251–275 (2018). https://doi.org/10.1504/ijaip.2019.10021825

    Article  Google Scholar 

  55. M. Weiser, The computer for the 21st century. Sci. Am. 265(3), 94–104 (1991). https://doi.org/10.1038/scientificamerican0991-94

  56. R.S. Raji, Smart networks for control. IEEE Spectrum 31, 49–55 (1994)

    Google Scholar 

  57. M. Gupta, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, H. Verma, P. Singhal, A. Singh, Comparative study of trends observed during different medications by subjects under EMG & GSR biofeedback, in ICSMSIC-2019, ABESEC, Ghaziabad, 8–9 March 2019. IJITEE 8(6S), 748–756 (2019). https://www.ijitee.org/download/volume-8-issue-6S/

  58. P. Singhal, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, M. Gupta, P. Singhal, M. Gulati, Statistical analysis of exponential and polynomial models of EMG & GSR biofeedback for correlation between subjects medications movement & medication scores, in ICSMSIC-2019, ABESEC, Ghaziabad, 8–9 March 2019. IJITEE 8(6S), 625–635 (2019). https://www.ijitee.org/download/volume-8-issue-6S/

  59. P. Magrassi, T. Berg, A world of smart objects. Gartner research report, R-17-2243 (12 August 2002)

    Google Scholar 

  60. H.C. Tsai, H. Cohly, D.K. Chaturvedi, Towards the consciousness of the mind, in Towards a Science of Consciousness, Dayalbagh Conference Proceeding, Agra, India, 2013

    Google Scholar 

  61. H. Saini, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, M. Gupta, H. Verma, An optimized biofeedback EMG and GSR biofeedback therapy for chronic TTH on SF-36 scores of different MMBD modes on various medical symptoms, Hybrid Machine Intelligence for Medical Image Analysis. Studies in Computational Intelligence, vol. 841 (Springer, Singapore, 2019) (chapter 8). https://doi.org/10.1007/978-981-13-8930-6_8. ISBN: 978-981-13-8929-0

  62. S. Nikan, F. Gwadry-Sridhar, M. Bauer, Machine learning application to predict the risk of coronary artery atherosclerosis, in International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, 2016, pp. 34–39. https://doi.org/10.1109/csci.2016.0014

  63. P. Magrassi, Why a universal RFID infrastructure would be a good thing. Gartner research report, G00106518 (2 May 2002)

    Google Scholar 

  64. A. Singh, R. Rastogi, D.K. Chaturvedi, S. Satya, N. Arora, A. Sharma, A. Singh, Intelligent personality analysis on indicators in IoT-MMBD enabled environment, in Multimedia Big Data Computing for IoT Applications: Concepts, Paradigms, and Solutions (Springer, Singapore, 2019), pp. 185–215 (chapter 7). https://doi.org/10.1007/978-981-13-8759-3_7

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Author information

Authors and Affiliations

  1. ABES Engineering College, Ghaziabad, UP, 201009, India

    Rohit Rastogi

  2. Dayalbagh Educational Institute, Agra, UP, 282005, India

    D. K. Chaturvedi

  3. Indian Institute of Technology, Delhi, 110016, India

    Santosh Satya

  4. Indian Institute of Technology, Roorkee, 247667, India

    Navneet Arora

Authors
  1. Rohit Rastogi
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  2. D. K. Chaturvedi
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  3. Santosh Satya
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  4. Navneet Arora
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Corresponding author

Correspondence to Rohit Rastogi .

Editor information

Editors and Affiliations

  1. Bharati Vidyapeeth's Institute of Computer Applications and Management, New Delhi, Delhi, India

    Vishal Jain

  2. Lord Buddha Education Foundation, Kathmandu, Nepal

    Jyotir Moy Chatterjee

Appendix

Appendix

Implementation—Web Application

Included here is the implementation of web application through feature driven development. Each activity of feature driven development is discussed with artifacts produced during that activity.

The web application has been developed using Django framework of python. It utilizes the MVT (Model View Template) architecture.

Development of Overall Model

During this activity of feature driven development, software requirement specification document was prepared for capturing the requirements. ER Diagram and requirement specification document was designed. After that, for the completion of this activity, a domain object model was prepared along with the overall application architecture.

Functional Specifications

Included in this section are the functional/non-functional requirements of the systems along with the use-cases and wireframes.

Functional Requirements

  • The system allows users to predict heart disease.

  • The system allows users to create an account and login.

  • The system allows the users to update their profile and password.

  • The system provides login for admin.

  • The system should allow administrator to monitor and remove inappropriate datasets and code.

Non-functional Requirements

  • The website should be responsive and have consistent across different screen sizes and resolutions.

  • The website should provide user information about different values used during the prediction.

Architecture

The major components of Django project architecture are models, views and templates along with urls.py, settings.py, admin.py and forms.py.

Our Simulation architecture has been described as follows:

Figure 20 shows the architecture of our project and how different files are distributed in different directories.

Fig. 20
figure 20

Simulation of heart disease predictor architecture

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Models

A model is a definitive data source that includes the underlying context and behavior of the data. A model is usually a table in a database. Each part of the table in the database is a model property. Django provides a set of automatic programming interfaces (APIs) for database applications for the convenience of the user.

View

The file view is short. This file contains a Python function that receives a web request and returns a web response. The answer is an XML document or HTML content or a “404 error”. The function logic of the function is not optional until it returns the desired response. To link a view function to a specific URL, you must use a structure called URLconf that maps the URL to a display function.

Template

Django templates are simple text files that can generate text-based templates such as HTML and XML. The template contains tags and variables. When evaluating the pattern, the variable is replaced with the result. Pattern logic is controlled by tags. You can also change variables using filters. For example, a small filter can convert a variable from uppercase to lowercase.

Figures 21 and 22 show how website and Analysis works. It shows how a request is sent and response is sent back.

Fig. 21
figure 21

Experimental working of heart disease predictor

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Fig. 22
figure 22

The flow chart of prediction system

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The working of a template is as follows. A request for a resource is made by any user from the template. Than Django works as a controller and check the availability of the resource in the URL. If the URL matches with any view than Django returns it to the template as a response.

Figure 23 shows the Cleveland data set used and machine learning algorithm applied by python software.

Fig. 23
figure 23

Dataset used and analysis by ML for disease prediction

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Documentation (User Manual):

For Users

Steps that how the system is executed:

Firstly the user has to connect his phone to internet by the medium of mobile data pack or by the medium of Wi-Fi.

Then the user has to download or clone the project. Steps to run

  • create virtual env ex. mkvirtualenv mytest_env (optional)

  • pip install -r requirements.txt

  • python manage.py migrate

  • python manage.py runserver.

Steps to execute Registration and Login

  • Then the user must register into the application using his basic personal details like username and email.

  • If the user is already registered, then the user can log into the application using his login credentials including username and password.

Prediction of Heart Risk

  • Now to predict the risk of heart disease, user can enter the values of various parameters on the basis of which his risk factor will be calculated.

  • After entering all the values, click on Predict button.

  • The page will be reloaded and the result will be shown according to 4 ML.

  • If result is 1, user has risk of heart disease. If result is 0, user does not have a risk of heart disease.

  • If two or more models give result as 1, the user has a risk of heart disease.

  • The user can also view his profile and previous predicted results by clicking on Profile tab.

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Rastogi, R., Chaturvedi, D.K., Satya, S., Arora, N. (2020). Intelligent Heart Disease Prediction on Physical and Mental Parameters: A ML Based IoT and Big Data Application and Analysis. In: Jain, V., Chatterjee, J. (eds) Machine Learning with Health Care Perspective. Learning and Analytics in Intelligent Systems, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-40850-3_10

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