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Real-time monitoring system for early prediction of heart disease using Internet of Things

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Abstract

The diagnosis of heart disease is found to be a serious concern, so the diagnosis has to be done remotely and regularly to take the prior action. In the present years, the diagnosis of heart disease has become a key research area for researchers and many models have been proposed in recent years. The diagnosis of heart disease can be done using optimization algorithms, and it provides results with good efficiency. The main objective of this paper is to propose a hybrid fuzzy-based decision tree algorithm for the process of prediction of heart disease at an early stage through the continuous and remote patient monitoring system. The results obtained from the proposed algorithm are compared with the various number of classifier algorithms like decision tree J48, naïve Bayes, GA with FCM, KNN with NB, ANN, SVM with fuzzy in which the proposed HFDT algorithm provides better accuracy of 98.30%. From the above-obtained results, the proposed hybrid fuzzy-based decision tree algorithm efficiently predicts heart disease compared to the other classifier algorithms in the literature. The proposed work is implemented in the MATLAB environment using the heart disease dataset.

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References

  • Ahilan A, Manogaran G, Raja C, Kadry S, Kumar SN, Kumar CA, ... Murugan NS (2019) Segmentation by fractional order darwinian particle swarm optimization based multilevel thresholding and improved lossless prediction based compression algorithm for medical images. IEEE Access7, 89570-89580

  • Austin PC, Tu JV et al (2013) Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. J Clinic Epidemiol 66(4):398–407

    Article  Google Scholar 

  • Babaoglu I, Baykan OK, Aygul N, Ozdemir K, Bayrak M (2009) Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization. Exp Syst Appl 36(2):2562–2566

    Article  Google Scholar 

  • Babu GC, Shantharajah SP (2018) Survey on data analytics techniques in healthcare using IOT platform. Int J Reason-Based Intell Syst 10(3–4):183–196

    Google Scholar 

  • Babu GC, Shantharajah SP (2021) Remote health patient monitoring system for early detection of heart disease. Int J Grid High Perform Comput (IJGHPC) 13(2):118–130

    Article  Google Scholar 

  • Babu GC, Shantharajah SP (2019) Optimal body mass index cutoff point for cardiovascular disease and high blood pressure. Neural Comput Appl 31(5):1585–1594

    Article  Google Scholar 

  • Devi GU, Priyan MK, Gokulnath C (2018) Wireless camera network with enhanced SIFT algorithm for human tracking mechanism. Int J Internet Tech Secured Trans 8(2):185–194

    Article  Google Scholar 

  • Eom JH, Kim SC et al (2008) AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction. Exp Syst Appl 34(4):2479–2008

    Article  Google Scholar 

  • Garbhapu VV, Gopalan S (2017) IoT based low cost single sensor node remote health monitoring system. Procedia Comput Sci 113:408–415

    Article  Google Scholar 

  • Gokulnath CB, Shantharajah SP (2019) An optimized feature selection based on genetic approach and support vector machine for heart disease. Clust Comput 22(6):14777–14787

    Article  Google Scholar 

  • Guidi G, Pettenati MC, Melillo P, Iadanza E (2014) A machine learning system to improve heart failure patient Assistance. IEEE J Biomed Health Inf 18(6):1750–1756

    Article  Google Scholar 

  • Huang MJ, Chen MY, Lee SC (2007) Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis. Exp Syst Appl 32(3):856–867

    Article  Google Scholar 

  • Karsdorp PA, Kindt M, Rietveld S, Everaerd W, Mulder BJ (2009) False heart rate feedback and the perception of heart symptoms in patients with congenital heart disease and anxiety. Int J Behav Med 16(1):81–88

    Article  Google Scholar 

  • Khatibi V, Montazer GA (2010) A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment. J Exp Syst Appl 37:8536–8542

  • Kumar PM, Lokesh S, Varatharajan R, Babu GC, Parthasarathy P (2018) Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Futur Gener Comput Syst 86:527–534

    Article  Google Scholar 

  • Luukka P, Lampinen J (2010) A Classification method based on principal component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data sets. Computational Intelligence in Optimization. Springer, Berlin, pp 263–283

    Chapter  Google Scholar 

  • Manogaran G, Shakeel PM, Hassanein AS, Kumar PM, Babu GC (2018a) Machine learning approach-based gamma distribution for brain tumor detection and data sample imbalance analysis. IEEE Access 7:12–19

    Article  Google Scholar 

  • Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2018b) A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Gen Comput Syst 82:375–387

    Article  Google Scholar 

  • Markos GT, Themis PE et al (2008) Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling. J IEEE Trans Inform Tech Biomed 12(4)

  • Melillo P, Luca ND, Bracale M, Pecchia L (2013) Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability. IEEE J Biomed Health Inf 17(3):727–733

    Article  Google Scholar 

  • Muthukaruppan S, Er MJ (2012) A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. J Exp Syst Appl 39:11657–11665

    Article  Google Scholar 

  • Nahar J, Imam T et al (2013) Association rule mining to detect factors which contribute to heart disease in males and females. Expert Syst Appl 40(4):1086–1093

    Article  Google Scholar 

  • Nahar J, Imam T, Tickle KS, Chen YPP (2013) Computational intelligence for heart disease diagnosis: a medical knowledge driven approach. Expert Syst Appl 40(1):96–104

    Article  Google Scholar 

  • Nawi NM, Ghazali R et al (2010) The development of improved back-propagation neural networks algorithm for predicting patients with heart disease. International conference on information computing and applications. Springer, Berlin, pp 317–324

    Chapter  Google Scholar 

  • Ordonez C (2006) Association rule discovery with the train and test approach for heart disease prediction. IEEE Trans Inform Tech Biomed 10(2):334–343

    Article  Google Scholar 

  • Ozcift A, Gulten A (2011) Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Comput Methods Prog Biomed 104(3):443–451

    Article  Google Scholar 

  • Padmavathy TV, Vimalkumar MN, Nagarajan S, Babu GC, Parthasarathy P (2018) Performance analysis of pre-cancerous mammographic image enhancement feature using non-subsampled shearlet transform. Multimedia Tools Appl 1–1

  • Parthiban G, Srivatsa SK (2012) Applying machine learning methods in diagnosing heart disease for diabetic patients. Int J Appl Inf Syst 3(7):25–30

    Google Scholar 

  • Polat K, Güneş S (2007) A hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRS. Comput Methods Prog Biomed 88(2):164–174

    Article  Google Scholar 

  • Polat K, Şahan S, Güneş S (2007) Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing. Exp Syst Appl 32(2):625–631

    Article  Google Scholar 

  • Sarkar BB, Paul S, Cornel B, Rohatinovici N, Chaki N (2016) Personal health record management system using Hadoop framework: an application for smarter health care. International workshop soft computing applications. Springer, Berlin, pp 385–393

    Google Scholar 

  • Setiawan NA et al (2008) A comparative study of imputation methods to predict missing attribute values in coronary heart disease data set. J Dep Elect Electron Eng 21, 266–269

  • Singh RS, Saini BS, Sunkaria RK (2018) Detection of coronary artery disease by reduced features and extreme learning machine. Clujul Med 91(2):166

    Google Scholar 

  • Son C-S, Kim Y-N et al (2012) Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches. J Biomed Inform 45:999–1008

    Article  Google Scholar 

  • Turan RG, Bozdag-t I et al (2011) Improved functional activity of bone marrow derived circulating progenitor cells after intra coronary freshly isolated bone marrow cells transplantation in patients with ischemic heart disease. Stem Cell Rev Rep 7(3):646–656

    Article  Google Scholar 

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Acknowledgements

This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman Universiry through the Fast-track Research Funding Program.

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Correspondence to Shakila Basheer.

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Communicated by Vicente Garcia Diaz.

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Basheer, S., Alluhaidan, A.S. & Bivi, M.A. Real-time monitoring system for early prediction of heart disease using Internet of Things. Soft Comput 25, 12145–12158 (2021). https://doi.org/10.1007/s00500-021-05865-4

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