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An enhanced approach for analyzing the performance of heart stroke prediction with machine learning techniques

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Abstract

The heart is one of the most vital organs in our body and crucial for proper bodily function, an unfit heart can seriously affect fitness, lifestyle and severely decrease the expected lifetime of an individual making a healthy heart necessary for survival. An early detection system for signs of a heart attack must be implemented in light of the alarming rise in the number of heart attacks in children and young adults. There has to be a method in place that is both convenient and accurate in forecasting the likelihood of a cardiac condition for the average person, such as the ECG. It has recently become easier to predict heart attacks due to machine learning (ML). Traditional prediction models and methodologies, on the other hand, are inadequate for gathering fundamental data because of their inability to imitate the high quality of mapping negative medical features. We forecast the survival of a cardiac patient using enhanced machine learning. Predicting a patient's risk of mortality from heart failure is based on information such as gender, age, blood pressure, kind of job, blood glucose, and body mass index. Support Vector Machine (SVM), Random Forest (RF), Navies Bayes (NB), Logistic Regression (LR), and Decision Tree (DT) are just a few of the machine learning-based classification algorithms that have been built and tested. The results of the experiments show that using 80% training and 20% testing, SVM can predict heart disease with an accuracy of 96.0%.

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References

  1. Bhatt CM, Patel P, Ghetia T, Mazzeo PL (2023) Effective heart disease prediction using machine learning techniques. Algorithms 16:88

    Article  Google Scholar 

  2. Dev S, Wang H, Nwosu CS, Jain N, Veeravalli B, John D (2022) A predictive analytics approach for stroke prediction using machine learning and neural networks. Healthc Anal 2:100032

    Article  Google Scholar 

  3. Sailasya G, Kumari GLA (2021) Analyzing the performance of stroke prediction using ML classification algorithms. Int J Adv Comput Sci Appl 12:539–545

    Google Scholar 

  4. Govindarajan P, Soundarapandian RK, Gandomi AH, Patan R, Jayaraman P, Manikandan R (2020) Classification of strokedisease using machine learning algorithms. Neural Comput Appl 32:817–828

    Article  Google Scholar 

  5. Nwosu, C.S.; Dev, S.; Bhardwaj, P.; Veeravalli, B.; John, D. Predicting stroke from electronic health records. In Proceedings of the2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany,23–27 July 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 5704–5707.

  6. Lee H, Lee EJ, Ham S, Lee HB, Lee JS, Kwon SU, Kim JS, Kim N, Kang DW (2020) Machine learning approach to identifystroke within 4.5 hours. Stroke 51:860–866

    Article  Google Scholar 

  7. Rexrode KM, Madsen TE, Yu AY, Carcel C, Lichtman JH, Miller EC (2022) The impact of sex and gender on stroke. Circ Res 130:512–528

    Article  Google Scholar 

  8. Dritsas E, Trigka M (2022) Stroke risk prediction with machine learning techniques. Sensors 22:4670

    Article  Google Scholar 

  9. Howard G (2021) Rural-urban differences in stroke risk. Prev Med 152:106661

    Article  Google Scholar 

  10. Cai Y, Wang C, Di W, Li W, Liu J, Zhou S (2020) Correlation between blood glucose variability and the risk of death in patientswith severe acute stroke. Rev Neurol 176:582–586

    Article  Google Scholar 

  11. Elsayed S, Othman M (2021) The effect of body mass index (BMI) on the mortality among patients with stroke. Eur J Mol Clin Med 8:181–187

    Google Scholar 

  12. Nusinovici S, Tham YC, Yan MYC, Ting DSW, Li J, Sabanayagam C, Wong TY, Cheng CY (2020) Logistic regression was asgood as machine learning for predicting major chronic diseases. J Clin Epidemiol 122:56–69

    Article  Google Scholar 

  13. Cunningham P, Delany SJ (2021) k-Nearest neighbour classifiers-A Tutorial. ACM Comput Surv (CSUR) 54:1–25

    Article  Google Scholar 

  14. Deepa N, Prabadevi B, Maddikunta PK, Gadekallu TR, Baker T, Khan MA, Tariq U (2021) An AI-based intelligent system forhealthcare analysis using Ridge-Adaline Stochastic Gradient Descent Classifier. J Supercomput 77:1998–2017

    Article  Google Scholar 

  15. Shankar K, Zhang Y, Liu Y, Wu L, Chen CH (2020) Hyperparameter tuning deep learning for diabetic retinopathy fundus imageclassification. IEEE Access 8:118164–118173

    Article  Google Scholar 

  16. Rajagopal S, Kundapur PP, Hareesha KS (2020) A stacking ensemble for network intrusion detection using heterogeneous datasets. Secur Commun Netw 2020:4586875

    Article  Google Scholar 

  17. Sharma C, Sharma S, Kumar M, Sodhi A (2022) Early stroke prediction using machine learning. Int Conf Decis Aid Sci and Appl (DASA) 2022:890–894

    Google Scholar 

  18. A. K. Uttam, "Analysis of Uneven Stroke Prediction Dataset using Machine Learning," 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS), 2022, pp. 1209–1213.

  19. Liu J, Chou EL, Lau KK, Woo PY, Li J (2022) Chan KH Machine learning algorithms identify demographics, dietary features, and blood biomarkers associated with stroke records. J Neurolog Sci 440:120335

    Article  Google Scholar 

  20. Liu Y, Ma B, Wang Y (2021) Study on prediction model of stroke riskbased on decision tree and regression model. IEEE Int Conf Big Data (Big Data) 2021:4798–4801. https://doi.org/10.1109/BigData52589.2021.9671409

    Article  Google Scholar 

  21. Sivapalan G, Nundy K, Dev S, Cardiff B, Deepu J (2022) ANNet: a lightweight neuralnetwork for ECG anomaly detection in IoT edge sensors. IEEE Trans Biomed Circuits Syst. https://doi.org/10.1109/TBCAS.2021.3137646

    Article  Google Scholar 

  22. Koh HC, Tan G et al (2011) Data mining applications in healthcare. J Healthc Inf Manag 19(2):65

    Google Scholar 

  23. Yoo I, Alafaireet P, Marinov M, Pena-Hernandez K, Gopidi R, Chang J-F, Hua L (2012) Data mining in healthcare and biomedicine: a survey of the literature. J Med Syst 36(4):2431–2448

    Article  Google Scholar 

  24. Meschia JF, Bushnell C, Boden-Albala B, Braun LT, Bravata DM, Chaturvedi S, Creager MA, Eckel RH, Elkind MS, Fornage M et al (2014) Guidelines for the primary prevention of stroke: a statement for healthcare professionals from the American heart association/American stroke association. Stroke 45(12):3754–3832

    Article  Google Scholar 

  25. Harmsen P, Lappas G, Rosengren A, Wilhelmsen L (2006) Long-term risk factors for stroke: twenty-eight years of follow-up of 7457 middle-aged men in Goteborg, Sweden. Stroke 37(7):1663–1667

    Article  Google Scholar 

  26. C.S. Nwosu, S. Dev, P. Bhardwaj, B. Veeravalli, D. John, Predicting stroke from electronic health records, in: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019, pp.5704–5707

  27. Pathan MS, Jianbiao Z, John D, Nag A, Dev S (2020) identifying stroke indicatorsusing rough sets. IEEE Access 8:210318–210327

    Article  Google Scholar 

  28. X. Li, H. Liu, X. Du, P. Zhang, G. Hu, G. Xie, S. Guo, M. Xu, X. Xie,Integrated machine learning approaches for predicting ischemic stroke andthromboembolism in atrial fibrillation, in: AMIA Annual Symposium Proceedings, 2016, American Medical Informatics Association, 2016, p. 799.

  29. García S, Luengo J, Herrera F (2016) Tutorial on practical tips of the most influentialdata preprocessing algorithms in data mining. Knowl-Based Syst 98:1–29

    Article  Google Scholar 

  30. Goldstein BA, Navar AM, Pencina MJ, Ioannidis J (2017) Opportunities andchallenges in developing risk prediction models with electronic health recordsdata: a systematic review. J Am Med Inform Assoc 24(1):198–208

    Article  Google Scholar 

  31. Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433–459

    Article  Google Scholar 

  32. Ozcan M, Peker S (2023) A classification and regression tree algorithm for heart disease modeling and prediction. Healthc Anal 3:100130

    Article  Google Scholar 

  33. F. Orlandi, A. Meehan, M. Hossari, S. Dev, D. O’Sullivan, T. AlSkaif, Interlinking heterogeneous data for smart energy systems, in: 2019 International Conference on Smart Energy Systems and Technologies (SEST), IEEE, 2019, pp. 1–6.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Odisha University of Technology and Research, Bhubaneswar, India

    Indrani Mishra & Subasish Mohapatra

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  1. Indrani Mishra
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  2. Subasish Mohapatra
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Correspondence to Indrani Mishra.

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Mishra, I., Mohapatra, S. An enhanced approach for analyzing the performance of heart stroke prediction with machine learning techniques. Int. j. inf. tecnol. 15, 3257–3270 (2023). https://doi.org/10.1007/s41870-023-01321-8

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  • DOI: https://doi.org/10.1007/s41870-023-01321-8

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