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Smart Prediction System for Heart Diseases Using Machine Learning Algorithms

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IoT, Big Data and AI for Improving Quality of Everyday Life: Present and Future Challenges

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1104))

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Abstract

Health care industry is one of the most important and fastest growing industries in the world right now. Heart disease encompasses a variety of disorders that affect the heart and has become the leading cause of mortality globally in recent decades. Online healthcare services can be deployed using latest and advanced IoT and sensing technologies. With the help of cloud computing and data that IoT devices generate, healthcare industries can get huge assistance. Due to this advancement of the IoT field, the sensors for monitoring heart rate are growing in availability to patients. It is linked to a number of risk factors as well as a pressing need for precise, reliable, and practical ways to early diagnosis and illness treatment. Identifying the risk factors for heart disease aids health care providers in identifying people who are at increased risk for heart disease. Need for building smart and intelligent systems to predict the risk of heart disease at an early stage is one of the major concerns of the researchers. In this paper we tend to solve this problem using a data-centric approach. After training the dataset with various supervised machine learning algorithms, results tell us that the highest accuracy is achieved with K-nearest neighbor and Random Forest Classifier. The model proposed can later be used with IoT sensors that will monitor the health of patients similar to the parameters used in the data.

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References

  1. ACC. (2020). Cardiovascular Disease Burden, Death Are Rising Around the World. https://www.acc.org/about-acc/press-releases/2020/12/09/18/30/cvd-burden-and-deaths-rising-around-the-world. Accessed 04 Apr 2021

  2. Ahmed, F. (2017). An Internet of Things (IoT) application for predicting the quantity of future heart attack patients. International Journal of Computer Applications, 164(6), 36–40.

    Article  Google Scholar 

  3. Anbarasi, M., Anupriya, E., & Iyengar, N. C. S. N. (2010). Enhanced prediction of heart disease with feature subset selection using genetic algorithm. International Journal of Engineering Science and Technology, 2(10), 5370–5376.

    Google Scholar 

  4. Chadha, R., & Mayank, S. (2016). Prediction of heart disease using data mining techniques. CSI Transactions on ICT, 4(2–4), 193–198.

    Article  Google Scholar 

  5. Chaurasia, V., & Pal, S. (2013). Early prediction of heart diseases using data mining techniques. Caribbean Journal of Science and Technology, 1, 208–217.

    Google Scholar 

  6. Cheng, C. A., & Chiu, H. W. (2017). An artificial neural network model for the evaluation of carotid artery stenting prognosis using a national-wide database. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2566–2569). IEEE.

    Google Scholar 

  7. Deekshatulu, B. L., & Chandra, P. (2013). Classification of heart disease using k-nearest neighbor and genetic algorithm. Procedia Technology, 10, 85–94.

    Article  Google Scholar 

  8. Deepika, K., & Seema, S. (2016). Predictive analytics to prevent and control chronic diseases. In 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) (pp. 381–386). IEEE.

    Google Scholar 

  9. Dwivedi, A. K. (2018). Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Computing and Applications, 29(10), 685–693.

    Article  Google Scholar 

  10. Gárate-Escamila, A. K., El Hassani, A. H., & Andrès, E. (2020). Classification models for heart disease prediction using feature selection and PCA. Informatics in Medicine Unlocked, 19, 100330.

    Article  Google Scholar 

  11. Heart Disease Data Set, UCI Machine Learning Repository.https://archive.ics.uci.edu/ml/datasets/heart+disease. Accessed 04 Apr 2021

  12. Işın, A., Direkoğlu, C., & Şah, M. (2016). Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Computer Science, 102, 317–324.

    Article  Google Scholar 

  13. Jabbar, M. A., Deekshatulu, B. L., & Chandra, P. (2012). An evolutionary algorithm for heart disease prediction. In International Conference on Information Processing (pp. 378–389). Springer, Berlin, Heidelberg.

    Google Scholar 

  14. Kannan, R., & Vasanthi, V. (2019). Machine learning algorithms with ROC curve for predicting and diagnosing heart disease. In Soft computing and medical bioinformatics (pp. 63–72). Springer, Singapore.

    Google Scholar 

  15. Karayılan, T., & Kılıç, Ö. (2017). Prediction of heart disease using neural network. In 2017 International Conference on Computer Science and Engineering (UBMK) (pp. 719–723). IEEE.

    Google Scholar 

  16. Katarya, R., & Meena, S. K. (2021). Machine learning techniques for heart disease prediction: A comparative study and analysis. Health and Technology, 11(1), 87–97.

    Article  Google Scholar 

  17. Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and Structural Biotechnology Journal, 15, 104–116.

    Article  Google Scholar 

  18. Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8–17.

    Article  Google Scholar 

  19. Latha, C. B. C., & Jeeva, S. C. (2019). Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Informatics in Medicine Unlocked, 16, 100203.

    Article  Google Scholar 

  20. Mahboob, T., Irfan, R., & Ghaffar, B. (2017). Evaluating ensemble prediction of coronary heart disease using receiver operating characteristics. In 2017 Internet Technologies and Applications (ITA) (pp. 110–115). IEEE.

    Google Scholar 

  21. Maji, S., & Arora, S. (2019). Decision tree algorithms for prediction of heart disease. In Information and communication technology for competitive strategies (pp. 447–454). Springer, Singapore.

    Google Scholar 

  22. Nahar, J., Imam, T., Tickle, K. S., & Chen, Y. P. P. (2013). Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications, 40(4), 1086–1093.

    Article  Google Scholar 

  23. Otoom, A. F., Abdallah, E. E., Kilani, Y., Kefaye, A., & Ashour, M. (2015). Effective diagnosis and monitoring of heart disease. International Journal of Software Engineering and Its Applications, 9(1), 143–156.

    Google Scholar 

  24. Palaniappan, R., Sundaraj, K., & Ahamed, N. U. (2013). Machine learning in lung sound analysis: A systematic review. Biocybernetics and Biomedical Engineering, 33(3), 129–135.

    Article  Google Scholar 

  25. Parthiban, G., & Srivatsa, S. K. (2012). Applying machine learning methods in diagnosing heart disease for diabetic patients. International Journal of Applied Information Systems (IJAIS), 3(7), 25–30.

    Article  Google Scholar 

  26. Pathak, A. K., & Valan, J. A. (2020). A predictive model for heart disease diagnosis using fuzzy logic and decision tree. In Smart computing paradigms: New progresses and challenges (pp. 131–140). Springer, Singapore.

    Google Scholar 

  27. Rani, S., & Masood, S. (2020). Predicting congenital heart disease using machine learning techniques. Journal of Discrete Mathematical Sciences and Cryptography, 23(1), 293–303.

    Article  Google Scholar 

  28. Rao, S. N., Shenoy, P., & Gopalakrishnan, M. (2018). Applicability of the Cleveland clinic scoring system for the risk prediction of acute kidney injury after cardiac surgery in a South Asian cohort. Indian Heart Journal, 70(4), 533–537.

    Article  Google Scholar 

  29. Raza, K. (2019). Improving the prediction accuracy of heart disease with ensemble learning and majority voting rule. In U-Healthcare Monitoring Systems (pp. 179–196). Academic Press.

    Google Scholar 

  30. Shah, D., Patel, S., & Bharti, S. K. (2020). Heart disease prediction using machine learning techniques. SN Computer Science, 1(6), 1–6.

    Article  Google Scholar 

  31. Statlog (Heart) Data Set, UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/statlog+(heart) Accessed 04 Apr 2021

  32. Subramaniyam, M., Singh, D., Park, S. J., Kim, S. E., Kim, D. J., Im, J. N., Min, S. N., et al. (2018). IoT based wake-up stroke prediction-Recent trends and directions. In IOP Conference Series: Materials Science and Engineering (Vol. 402, No. 1, p. 012045). IOP Publishing.

    Google Scholar 

  33. Tougui, I., Jilbab, A., & El Mhamdi, J. (2020). Heart disease classification using data mining tools and machine learning techniques. Health and Technology, 10, 1137–1144.

    Article  Google Scholar 

  34. Vembandasamy, K., Sasipriya, R., & Deepa, E. (2015). Heart diseases detection using Naive Bayes algorithm. International Journal of Innovative Science, Engineering & Technology, 2(9), 441–444.

    Google Scholar 

  35. WHO. (2021). Cardiovascular disease (CVDs). https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed 04 Apr 2021

  36. Yu, B., & Xu, Z. B. (2008). A comparative study for content-based dynamic spam classification using four machine learning algorithms. Knowledge-Based Systems, 21(4), 355–362.

    Article  Google Scholar 

  37. Zhao, W., Wang, C., & Nakahira, Y. (2011). Medical application on internet of things. In IET International Conference on Communication Technology and Application (ICCTA 2011) (pp. 660–665). IET.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science and Engineering and Information Technology, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, India

    Hiteshwar Singh, Tushar Gupta & Harshita Sharma

  2. Narsee Monjee Institute of Management Studies (NMIMS), School of Technology Management & Engineering (STME), Chandigarh, India

    Jagpreet Sidhu

Authors
  1. Hiteshwar Singh
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  2. Tushar Gupta
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  3. Harshita Sharma
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  4. Jagpreet Sidhu
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Contributions

All authors have equal contribution to this study and all authors have read and approved the final manuscript.

Corresponding author

Correspondence to Jagpreet Sidhu .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, NMIMS Deemed to be University, STME, Chandigarh, Chandigarh, India

    Pradeep Kumar Singh

  2. Institute of Computer Science, Polish Academy of Sciences, Warszawa, Poland

    Sławomir T. Wierzchoń

  3. Faculty of Mathematics, Physics and Informatics, University of Gdańsk, Gdańsk, Poland

    Wiesław Pawłowski

  4. DMS, Indian Institute of Technology, Hauz Khas, Delhi, India

    Arpan Kumar Kar

  5. Department of Computer Science and Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Yugal Kumar

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Singh, H., Gupta, T., Sharma, H., Sidhu, J. (2023). Smart Prediction System for Heart Diseases Using Machine Learning Algorithms. In: Singh, P.K., Wierzchoń, S.T., Pawłowski, W., Kar, A.K., Kumar, Y. (eds) IoT, Big Data and AI for Improving Quality of Everyday Life: Present and Future Challenges. Studies in Computational Intelligence, vol 1104. Springer, Cham. https://doi.org/10.1007/978-3-031-35783-1_16

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