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An Extensive Review of Machine Learning and Deep Learning Techniques on Heart Disease Classification and Prediction

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Abstract

Heart disease is a widespread global concern, underscoring the critical importance of early detection to minimize mortality. Although coronary angiography is the most precise diagnostic method, its discomfort and cost often deter patients, particularly in the disease's initial stages. Hence, there is a pressing need for a non-invasive and dependable diagnostic approach. In the contemporary era, machine learning has pervaded various aspects of human life, playing a significant role in revolutionizing the healthcare industry. Decision support systems based on machine learning, leveraging a patient's clinical parameters, offer a promising avenue for diagnosing heart disease. Early detection remains pivotal in mitigating the severity of heart disease. The healthcare sector generates vast amounts of patient and disease-related data daily. Unfortunately, practitioners frequently underutilize this valuable resource. To tap into the potential of this data for more precise heart disease diagnoses, a range of machine learning algorithms is available. Given the extensive research on automated heart disease detection systems, there is a need to synthesize this knowledge. This paper aims to provide a comprehensive overview of recent research on heart disease diagnosis by reviewing articles published by reputable sources between 2014 and 2022. It identifies challenges faced by researchers and proposes potential solutions. Additionally, the paper suggests directions for expanding upon existing research in this critical field.

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References

  1. Kumar R, Rani P (2020) Comparative analysis of decision support system for heart disease. Adv Math Sci J. https://doi.org/10.37418/amsj.9.6.15

    Article  Google Scholar 

  2. Rajkumar R, Anandakumar K, Bharathi A (2016) Coronary artery disease (CAD) prediction and classification—a survey. ARPN J Eng Appl Sci 11

  3. Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. https://doi.org/10.1007/s42979-021-00592-x

    Article  PubMed  PubMed Central  Google Scholar 

  4. Patel S, Patel A (2016) A big data revolution in health care sector: opportunities, challenges and technological advancements. Int J Inf Sci Tech. https://doi.org/10.5121/ijist.2016.6216

    Article  Google Scholar 

  5. Malakar AK, Choudhury D, Halder B et al (2019) A review on coronary artery disease, its risk factors, and therapeutics. J Cell Physiol. https://doi.org/10.1002/jcp.28350

    Article  PubMed  Google Scholar 

  6. Masetic Z, Subasi A (2016) Congestive heart failure detection using random forest classifier. Comput Methods Progr Biomed. https://doi.org/10.1016/j.cmpb.2016.03.020

    Article  Google Scholar 

  7. Ghadiri Hedeshi N, Saniee Abadeh M (2014) Coronary artery disease detection using a fuzzy-boosting PSO approach. Comput Intell Neurosci. https://doi.org/10.1155/2014/783734

    Article  PubMed  PubMed Central  Google Scholar 

  8. Bashir S, Qamar U, Khan FH, Javed MY (2014) MV5: a clinical decision support framework for heart disease prediction using majority vote based classifier ensemble. Arab J Sci Eng. https://doi.org/10.1007/s13369-014-1315-0

    Article  Google Scholar 

  9. Tomar D, Agarwal S (2014) Feature selection based least square twin support vector machine for diagnosis of heart disease. Int J Bio-Sci Bio-Technol. https://doi.org/10.14257/ijbsbt.2014.6.2.07

    Article  Google Scholar 

  10. Olaniyi EO, Oyedotun OK, Adnan K (2015) Heart diseases diagnosis using neural networks arbitration. Int J Intell Syst Appl 7:75–82

    Google Scholar 

  11. Marateb HR, Goudarzi S (2015) A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system. J Res Med Sci 20:214

    Article  PubMed  PubMed Central  Google Scholar 

  12. Khanna D, Sahu R, Baths V, Deshpande B (2015) Comparative study of classification techniques (SVM Logistic Regression and Neural Networks) to predict the prevalence of heart disease. Int J Mach Learn Comput. https://doi.org/10.7763/ijmlc.2015.v5.544

    Article  Google Scholar 

  13. Long NC, Meesad P, Unger H (2015) A highly accurate firefly based algorithm for heart disease prediction. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2015.06.024

    Article  Google Scholar 

  14. Miranda E, Irwansyah E, Amelga AY et al (2016) Detection of cardiovascular disease risk’s level for adults using naive bayes classifier. Healthc Inform Res. https://doi.org/10.4258/hir.2016.22.3.196

    Article  PubMed  PubMed Central  Google Scholar 

  15. Verma L, Srivastava S, Negi PC (2016) A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. J Med Syst. https://doi.org/10.1007/s10916-016-0536-z

    Article  PubMed  Google Scholar 

  16. Jabbar MA, Deekshatulu BL, Chandra P (2016) Prediction of heart disease using random forest and feature subset selection. In: Snášel V, Abraham A, Krömer P, Pant M, Muda A (eds) Advances in intelligent systems and computing. Springer, Cham

    Google Scholar 

  17. Liu X, Wang X, Su Q et al (2017) A hybrid classification system for heart disease diagnosis based on the RFRS method. Comput Math Methods Med. https://doi.org/10.1155/2017/8272091

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  18. Buchan K, Filannino M, Uzuner Ö (2017) Automatic prediction of coronary artery disease from clinical narratives. J Biomed Inform. https://doi.org/10.1016/j.jbi.2017.06.019

    Article  PubMed  PubMed Central  Google Scholar 

  19. Mdhaffar A, Bouassida Rodriguez I, Charfi K et al (2017) CEP4HFP: complex event processing for heart failure prediction. IEEE Trans Nanobiosci. https://doi.org/10.1109/TNB.2017.2769671

    Article  Google Scholar 

  20. Babic F, Olejar J, Vantova Z, Paralic J (2017) Predictive and descriptive analysis for heart disease diagnosis. In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017

  21. Davari Dolatabadi A, Khadem SEZ, Asl BM (2017) Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM. Comput Methods Progr Biomed. https://doi.org/10.1016/j.cmpb.2016.10.011

    Article  Google Scholar 

  22. Kumar SU, Inbarani HH (2017) Neighborhood rough set based ECG signal classification for diagnosis of cardiac diseases. Soft Comput. https://doi.org/10.1007/s00500-016-2080-7

    Article  Google Scholar 

  23. Shah SMS, Batool S, Khan I et al (2017) Feature extraction through parallel probabilistic principal component analysis for heart disease diagnosis. Phys A Stat Mech its Appl. https://doi.org/10.1016/j.physa.2017.04.113

    Article  Google Scholar 

  24. Qin CJ, Guan Q, Wang XP (2017) Application of ensemble algorithm integrating multiple criteria feature selection in coronary heart disease detection. Biomed Eng—Appl Basis Commun. https://doi.org/10.4015/S1016237217500430

    Article  Google Scholar 

  25. Nalluri MSR, Kannan K, Manisha M, Roy DS (2017) Hybrid disease diagnosis using multiobjective optimization with evolutionary parameter optimization. J Healthc Eng. https://doi.org/10.1155/2017/5907264

    Article  PubMed  PubMed Central  Google Scholar 

  26. Alizadehsani R, Hosseini MJ, Khosravi A et al (2018) Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries. Comput Methods Progr Biomed. https://doi.org/10.1016/j.cmpb.2018.05.009

    Article  Google Scholar 

  27. Verma L, Srivastava S, Negi PC (2018) An intelligent noninvasive model for coronary artery disease detection. Complex Intell Syst. https://doi.org/10.1007/s40747-017-0048-6

    Article  Google Scholar 

  28. Dhanaseelan R, Jeya Sutha M (2018) Diagnosis of coronary artery disease using an efficient hash table based closed frequent itemsets mining. Med Biol Eng Comput. https://doi.org/10.1007/s11517-017-1719-6

    Article  PubMed  Google Scholar 

  29. David HBF, Belcy SA (2018) Heart disease prediction using data mining techniques. ICTACT J SOFT Comput 9:1824–1830

    Google Scholar 

  30. Haq AU, Li JP, Memon MH et al (2018) A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mob Inf Syst. https://doi.org/10.1155/2018/3860146

    Article  Google Scholar 

  31. Vijayashree J, Sultana HP (2018) A machine learning framework for feature selection in heart disease classification using improved particle swarm optimization with support vector machine classifier. Progr Comput Softw. https://doi.org/10.1134/S0361768818060129

    Article  Google Scholar 

  32. Dwivedi AK (2018) Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2604-1

    Article  Google Scholar 

  33. Dogan MV, Grumbach IM, Michaelson JJ, Philibert RA (2018) Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham heart study. PLoS ONE. https://doi.org/10.1371/journal.pone.0190549

    Article  PubMed  PubMed Central  Google Scholar 

  34. Saqlain SM, Sher M, Shah FA et al (2019) Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines. Knowl Inf Syst. https://doi.org/10.1007/s10115-018-1185-y

    Article  Google Scholar 

  35. Abdar M, Książek W, Acharya UR et al (2019) A new machine learning technique for an accurate diagnosis of coronary artery disease. Comput Methods Prog Biomed. https://doi.org/10.1016/j.cmpb.2019.104992

    Article  Google Scholar 

  36. Ayatollahi H, Gholamhosseini L, Salehi M (2019) Predicting coronary artery disease: a comparison between two data mining algorithms. BMC Public Health. https://doi.org/10.1186/s12889-019-6721-5

    Article  PubMed  PubMed Central  Google Scholar 

  37. Latha CBC, Jeeva SC (2019) Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Informat Med Unlocked. https://doi.org/10.1016/j.imu.2019.100203

    Article  Google Scholar 

  38. Khennou F, Fahim C, Chaoui H, Chaoui NEH (2019) A machine learning approach using predictive analytics to identify and analyze high risks patients with heart disease. Int J Mach Learn Comput. https://doi.org/10.18178/ijmlc.2019.9.6.870

    Article  Google Scholar 

  39. Magesh G, Swarnalatha P (2021) Optimal feature selection through a cluster-based DT learning (CDTL) in heart disease prediction. Evol Intell. https://doi.org/10.1007/s12065-019-00336-0

    Article  Google Scholar 

  40. Khourdifi Y, Bahaj M (2019) Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization. Int J Intell Eng Syst. https://doi.org/10.22266/ijies2019.0228.24

    Article  Google Scholar 

  41. Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2923707

    Article  Google Scholar 

  42. Ali L, Niamat A, Khan JA et al (2019) An optimized stacked support vector machines based expert system for the effective prediction of heart failure. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2909969

    Article  Google Scholar 

  43. Li JP, Haq AU, Din SU et al (2020) Heart disease identification method using machine learning classification in E-healthcare. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3001149

    Article  PubMed  PubMed Central  Google Scholar 

  44. Fitriyani NL, Syafrudin M, Alfian G, Rhee J (2020) HDPM: an effective heart disease prediction model for a clinical decision support system. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3010511

    Article  Google Scholar 

  45. Almustafa KM (2020) Prediction of heart disease and classifiers’ sensitivity analysis. BMC Bioinformat. https://doi.org/10.1186/s12859-020-03626-y

    Article  Google Scholar 

  46. Tama BA, Im S, Lee S (2020) Improving an intelligent detection system for coronary heart disease using a two-tier classifier ensemble. Biomed Res Int. https://doi.org/10.1155/2020/9816142

    Article  PubMed  PubMed Central  Google Scholar 

  47. Terrada O, Hamida S, Cherradi B et al (2020) Supervised machine learning based medical diagnosis support system for prediction of patients with heart disease. Adv Sci Technol Eng Syst. https://doi.org/10.25046/AJ050533

    Article  Google Scholar 

  48. Jinny SV, Mate YV (2021) Early prediction model for coronary heart disease using genetic algorithms, hyper-parameter optimization and machine learning techniques. Health Technol (Berl). https://doi.org/10.1007/s12553-020-00508-4

    Article  Google Scholar 

  49. Joloudari JH, Joloudari EH, Saadatfar H et al (2020) Coronary artery disease diagnosis; ranking the significant features using a random trees model. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph17030731

    Article  PubMed  PubMed Central  Google Scholar 

  50. Mienye ID, Sun Y, Wang Z (2020) An improved ensemble learning approach for the prediction of heart disease risk. Informat Med Unlocked. https://doi.org/10.1016/j.imu.2020.100402

    Article  Google Scholar 

  51. Spencer R, Thabtah F, Abdelhamid N, Thompson M (2020) Exploring feature selection and classification methods for predicting heart disease. Digit Heal. https://doi.org/10.1177/2055207620914777

    Article  Google Scholar 

  52. Gazeloğlu C (2020) Prediction of heart disease by classifying with feature selection and machine learning methods. Prog Nutr. https://doi.org/10.23751/pn.v22i2.9830

    Article  Google Scholar 

  53. Budholiya K, Shrivastava SK, Sharma V (2020) An optimized XGBoost based diagnostic system for effective prediction of heart disease. J King Saud Univ—Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2020.10.013

    Article  Google Scholar 

  54. Amin MS, Chiam YK, Varathan KD (2019) Identification of significant features and data mining techniques in predicting heart disease. Telemat Inform. https://doi.org/10.1016/j.tele.2018.11.007

    Article  Google Scholar 

  55. Gárate-Escamila AK, Hajjam El Hassani A, Andrès E (2020) Classification models for heart disease prediction using feature selection and PCA. Informat Med Unlocked. https://doi.org/10.1016/j.imu.2020.100330

    Article  Google Scholar 

  56. Arul Jothi K, Subburam S, Umadevi V, Hemavathy K (2021) Heart disease prediction system using machine learning. Mater Today Proc. https://doi.org/10.1016/j.matpr.2020.12.901

    Article  Google Scholar 

  57. Valarmathi R, Sheela T (2021) Heart disease prediction using hyper parameter optimization (HPO) tuning. Biomed Signal Process Control. https://doi.org/10.1016/j.bspc.2021.103033

    Article  Google Scholar 

  58. Bahani K, Moujabbir M, Ramdani M (2021) An accurate fuzzy rule-based classification systems for heart disease diagnosis. Sci African. https://doi.org/10.1016/j.sciaf.2021.e01019

    Article  Google Scholar 

  59. Shorewala V (2021) Early detection of coronary heart disease using ensemble techniques. Informat Med Unlocked. https://doi.org/10.1016/j.imu.2021.100655

    Article  Google Scholar 

  60. Rani P, Kumar R, Ahmed NMOS, Jain A (2021) A decision support system for heart disease prediction based upon machine learning. J Reliab Intell Environ. https://doi.org/10.1007/s40860-021-00133-6

    Article  Google Scholar 

  61. Rani P, Kumar R, Jain A (2021) Coronary artery disease diagnosis using extra tree-support vector machine: ET-SVMRBF. Int J Comput Appl Technol. https://doi.org/10.1504/IJCAT.2021.119772

    Article  Google Scholar 

  62. Patro SP, Nayak GS, Padhy N (2021) Heart disease prediction by using novel optimization algorithm: a supervised learning prospective. Informat Med Unlocked. https://doi.org/10.1016/j.imu.2021.100696

    Article  Google Scholar 

  63. Louridi N, Douzi S, El Ouahidi B (2021) Machine learning-based identification of patients with a cardiovascular defect. J Big Data. https://doi.org/10.1186/s40537-021-00524-9

    Article  Google Scholar 

  64. Ghosh P, Azam S, Jonkman M et al (2021) Efficient prediction of cardiovascular disease using machine learning algorithms with relief and lasso feature selection techniques. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3053759

    Article  PubMed  Google Scholar 

  65. Nawaz MS, Shoaib B, Ashraf MA (2021) Intelligent cardiovascular disease prediction empowered with gradient descent optimization. Heliyon. https://doi.org/10.1016/j.heliyon.2021.e06948

    Article  PubMed  PubMed Central  Google Scholar 

  66. Chang V, Bhavani VR, Xu AQ, Hossain M (2022) An artificial intelligence model for heart disease detection using machine learning algorithms. Healthc Anal. https://doi.org/10.1016/j.health.2022.100016

    Article  Google Scholar 

  67. Archana KS, Sivakumar B, Kuppusamy R et al (2022) Automated cardioailment identification and prevention by hybrid machine learning models. Comput Math Methods Med. https://doi.org/10.1155/2022/9797844

    Article  PubMed  PubMed Central  Google Scholar 

  68. Nagavelli U, Samanta D, Chakraborty P (2022) Machine learning technology-based heart disease detection models. J Healthc Eng. https://doi.org/10.1155/2022/7351061

    Article  PubMed  PubMed Central  Google Scholar 

  69. Gao XY, Amin Ali A, Shaban Hassan H, Anwar EM (2021) Improving the accuracy for analyzing heart diseases prediction based on the ensemble method. Complexity. https://doi.org/10.1155/2021/6663455

    Article  Google Scholar 

  70. Verma P (2020) Ensemble models for classification of coronary artery disease using decision trees. Int J Recent Technol Eng. 8:940–944. https://doi.org/10.35940/ijrte.F7250.038620

    Article  Google Scholar 

  71. Javid I, Alsaedi AKZ, Ghazali R (2020) Enhanced accuracy of heart disease prediction using machine learning and recurrent neural networks ensemble majority voting method. Int J Adv Comput Sci Appl. https://doi.org/10.14569/ijacsa.2020.0110369

    Article  Google Scholar 

  72. Choi E, Schuetz A, Stewart WF, Sun J (2017) Using recurrent neural network models for early detection of heart failure onset. J Am Med Informat Assoc. https://doi.org/10.1093/jamia/ocw112

    Article  Google Scholar 

  73. Arabasadi Z, Alizadehsani R, Roshanzamir M et al (2017) Computer aided decision making for heart disease detection using hybrid neural network-genetic algorithm. Comput Methods Progr Biomed. https://doi.org/10.1016/j.cmpb.2017.01.004

    Article  Google Scholar 

  74. Samuel OW, Asogbon GM, Sangaiah AK et al (2017) An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2016.10.020

    Article  Google Scholar 

  75. Kim JK, Kang S (2017) Neural network-based coronary heart disease risk prediction using feature correlation analysis. J Healthc Eng. https://doi.org/10.1155/2017/2780501

    Article  PubMed  PubMed Central  Google Scholar 

  76. Caliskan A, Yuksel ME (2017) Classification of coronary artery disease data sets by using a deep neural network. EuroBiotech J. https://doi.org/10.24190/issn2564-615x/2017/04.03

    Article  Google Scholar 

  77. Poornima V, Gladis D (2018) A novel approach for diagnosing heart disease with hybrid classifier. Biomed Res. https://doi.org/10.4066/biomedicalresearch.38-18-434

    Article  Google Scholar 

  78. Malav A, Kadam K (2018) A hybrid approach for Heart Disease Prediction using Artificial Neural Network and K-means. Int J Pure Appl Math 118

  79. Tan JH, Hagiwara Y, Pang W et al (2018) Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2017.12.023

    Article  PubMed  Google Scholar 

  80. Miao KH, Miao JH (2018) Coronary heart disease diagnosis using deep neural networks. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2018.091001

    Article  Google Scholar 

  81. Ali L, Rahman A, Khan A et al (2019) An automated diagnostic system for heart disease prediction based on χ2 statistical model and optimally configured deep neural network. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2904800

    Article  Google Scholar 

  82. Meshref H (2019) Cardiovascular disease diagnosis: a machine learning interpretation approach. Int J Adv Comput Sci Appl. https://doi.org/10.14569/ijacsa.2019.0101236

    Article  Google Scholar 

  83. Verma L, Mathur MK (2019) Deep learning based model for decision support with case based reasoning. Int J Innov Technol Explor Eng 8

  84. Javeed A, Rizvi SS, Zhou S et al (2020) Heart risk failure prediction using a novel feature selection method for feature refinement and neural network for classification. Mob Inf Syst. https://doi.org/10.1155/2020/8843115

    Article  Google Scholar 

  85. Pan Y, Fu M, Cheng B et al (2020) Enhanced deep learning assisted convolutional neural network for heart disease prediction on the internet of medical things platform. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3026214

    Article  Google Scholar 

  86. Dutta A, Batabyal T, Basu M, Acton ST (2020) An efficient convolutional neural network for coronary heart disease prediction. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113408

    Article  Google Scholar 

  87. Paragliola G, Coronato A (2021) An hybrid ECG-based deep network for the early identification of high-risk to major cardiovascular events for hypertension patients. J Biomed Inform. https://doi.org/10.1016/j.jbi.2020.103648

    Article  PubMed  Google Scholar 

  88. Cherian RP, Thomas N, Venkitachalam S (2020) Weight optimized neural network for heart disease prediction using hybrid lion plus particle swarm algorithm. J Biomed Inform. https://doi.org/10.1016/j.jbi.2020.103543

    Article  PubMed  Google Scholar 

  89. Salhi Dhai Eddine and Tari A and KM-T (2021) Using machine learning for heart disease prediction. In: Senouci Mustapha Redaand Boudaren MEY and SF and MM (ed) Advances in computing systems and applications. Springer, Cham

  90. Murugesan S, Bhuvaneswaran RS, Khanna Nehemiah H et al (2021) Feature selection and classification of clinical datasets using bioinspired algorithms and super learner. Comput Math Methods Med. https://doi.org/10.1155/2021/6662420

    Article  PubMed  PubMed Central  Google Scholar 

  91. Bharti R, Khamparia A, Shabaz M et al (2021) Prediction of heart disease using a combination of machine learning and deep learning. Comput Intell Neurosci. https://doi.org/10.1155/2021/8387680

    Article  PubMed  PubMed Central  Google Scholar 

  92. Mehmood A, Iqbal M, Mehmood Z et al (2021) Prediction of heart disease using deep convolutional neural networks. Arab J Sci Eng. https://doi.org/10.1007/s13369-020-05105-1

    Article  Google Scholar 

  93. Koppu S, Maddikunta PKR, Srivastava G (2020) Deep learning disease prediction model for use with intelligent robots. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2020.106765

    Article  PubMed  PubMed Central  Google Scholar 

  94. Ali SA, Raza B, Malik AK et al (2020) An Optimally configured and improved deep belief network (OCI-DBN) approach for heart disease prediction based on Ruzzo–Tompa and stacked genetic algorithm. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2985646

    Article  PubMed  Google Scholar 

  95. Cleveland Dataset (1988) Cleveland Dataset. In: V.A. Med. Center, Long Beach Clevel. Clin. Found. https://archive.ics.uci.edu/ml/datasets/heart+disease

  96. SPECTF Dataset 2001 SPECTF Dataset. https://archive.ics.uci.edu/ml/datasets/SPECTF+Heart. Accessed 11 May 2022

  97. Z-Alizadeh Sani Dataset 2017 Z-Alizadeh Sani_Dataset. https://archive.ics.uci.edu/ml/datasets/Z-Alizadeh+Sani. Accessed 11 Apr 2022

  98. Rani P, Kumar R, Jain A (2022) A novel hybrid imputation method to predict missing values in medical datasets. In: Marriwala N, Tripathi C, Jain S, Kumar D (eds) Lecture notes in networks and systems. Springer, Singapore

    Google Scholar 

  99. Rani P, Kumar R, Jain A (2022) A hybrid approach for feature selection based on correlation feature selection and genetic algorithm. Int J Softw Innov. https://doi.org/10.4018/ijsi.292028

    Article  Google Scholar 

  100. Framingham Dataset Framingham_Dataset. https://www.kaggle.com/captainozlem/framingham-chd-preprocessed-data. Accessed 11 May 2022

  101. Statlog Dataset Statlog_Dataset. http://archive.ics.uci.edu/ml/datasets/statlog+(heart)

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Authors and Affiliations

  1. MMICTBM, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, Haryana, India

    Pooja Rani

  2. Department of Computer Engineering, MMEC, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, Haryana, India

    Rajneesh Kumar

  3. School of Computer Sciences, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Anurag Jain

  4. Electronics and Communication Engineering Department, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, Haryana, India

    Rohit Lamba & Karan Kumar

  5. Department of Computer Science & Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Ravi Kumar Sachdeva

  6. School of Computer Science, FEIS, University of Wollongong in Dubai, Dubai Knowledge Park, Dubai, UAE

    Manoj Kumar

  7. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Manoj Kumar

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Rani, P., Kumar, R., Jain, A. et al. An Extensive Review of Machine Learning and Deep Learning Techniques on Heart Disease Classification and Prediction. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10075-w

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