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2024 | OriginalPaper | Buchkapitel

Prediction of Heart Disease and Heart Failure Using Ensemble Machine Learning Models

verfasst von : Abdullah Al Maruf, Aditi Golder, Abdullah Al Numan, Md. Mahmudul Haque, Zeyar Aung

Erschienen in: Intelligent Systems

Verlag: Springer Nature Singapore

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Abstract

Heart disease, commonly referred to as cardiovascular disease and heart failure, has been the leading cause of mortality globally. Many risk factors for heart disease are associated with prompt access to reliable, dependable, and practical early diagnosis and disease management procedures. Identifying heart disease through early-stage signs is challenging in today’s global climate. If not caught in time, this could result in death. When there are no heart specialist doctors in remote, semi-urban, or rural areas, precise risk prediction and analysis might be critical in the early-stage identification of heart disorders. Machine learning (ML) and Deep learning (DL) approaches were employed in this study to assess massive volumes of complex medical data, supporting specialists in predicting heart illness and mortality from heart failure. This study used two datasets: one to forecast heart disease and the other to analyze and forecast death due to heart failure. Predicting cardiac illnesses using Artificial Neural Networks is 91.52% accurate (ANN). The bagging ensemble predicted heart failure with 90% accuracy. The primary contribution of this research is an ensemble strategy with high performance that multiple measurements have demonstrated to predict heart failure and cardiac disorders using ANN.

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Al-Shayea QK (2011) Artificial neural networks in medical diagnosis. Int J Comput Sci Issues 8(2):150–154

Alaa AM, Bolton T, Di Angelantonio E, Rudd JH, Van der Schaar M (2019) Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants. PLOS ONE 14(5):e0213653CrossRef

Amin MS, Chiam YK, Varathan KD (2019) Identification of significant features and data mining techniques in predicting heart disease. Telematics Inform 36:82–93CrossRef

Ananey-Obiri D, Sarku E (2020) Predicting the presence of heart diseases using comparative data mining and machine learning algorithms. Int J Comput Appl 176(11):17–21

Andreotti F, et al (2020) Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units. arXiv preprint arXiv:2007.08491

Ashraf M et al (2021) Prediction of cardiovascular disease through cutting-edge deep learning technologies: an empirical study based on TENSORFLOW, PYTORCH and KERAS. In: Gupta D, Khanna A, Bhattacharyya S, Hassanien AE, Anand S, Jaiswal A (eds) International Conference on Innovative Computing and Communications, vol 1165. AISC. Springer, Singapore, pp 239–255. https://doi.org/10.1007/978-981-15-5113-0_18CrossRef

Chen M, Hao Y, Hwang K, Wang L, Wang L (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5:8869–8879CrossRef

Chicco D, Jurman G (2020) Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Making 20(1):1–16CrossRef

Dinesh KG, Arumugaraj K, Santhosh KD, Mareeswari V (2018) Prediction of cardiovascular disease using machine learning algorithms. In: 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT). IEEE, pp 1–7

10.

Dwivedi AK (2018) Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput Appl 29(10):685–693CrossRef

11.

Ed-Daoudy A, Maalmi K (2019) Performance evaluation of machine learning based big data processing framework for prediction of heart disease. In: 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS). IEEE, pp 1–5

12.

Gazeloglu C (2020) Prediction of heart disease by classifying with feature selection and machine learning methods. Progr Nutrition 22(2):660–670

13.

Ghosh P et al (2021) Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques. IEEE Access 9:19304–19326CrossRef

14.

Ghwanmeh S, Mohammad A, Al-Ibrahim A (2013) Innovative artificial neural networks-based decision support system for heart diseases diagnosis. J Intell Learn Syst Appl 5(3)

15.

Goel S, Deep A, Srivastava S, Tripathi A (2019) Comparative analysis of various techniques for heart disease prediction. In: 2019 4th International Conference on Information Systems and Computer Networks (ISCON). IEEE, pp 88–94

16.

Gupta A, Kumar R, Arora HS, Raman B (2019) MIFH: a machine intelligence framework for heart disease diagnosis. IEEE Access 8:14659–14674CrossRef

17.

Haq AU, Li JP, Memon MH, Nazir S, Sun R (2018) A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mob Inf Syst 2018

18.

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 11(3)

19.

Jousilahti P, Vartiainen E, Tuomilehto J, Puska P (1999) Sex, age, cardiovascular risk factors, and coronary heart disease: a prospective follow-up study of 14 786 middle-aged men and women in Finland. Circulation 99(9):1165–1172CrossRef

20.

Karthick D, Priyadharshini B (2018) Predicting the chances of occurrence of cardio vascular disease (CVD) in people using classification techniques within fifty years of age. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, pp 1182–1186

21.

Kaur A (2017) A comprehensive approach to predict heart diseases using data mining. Int J Innov Eng Technol 8(2):2319–2358

22.

Kavitha M, Gnaneswar G, Dinesh R, Sai YR, Suraj RS (2021) Heart disease prediction using hybrid machine learning model. In: 2021 6th International Conference on Inventive Computation Technologies (ICICT). IEEE, pp 1329–1333

23.

Kodati S, Vivekanandam DR (2018) Analysis of heart disease using in data mining tools Orange and Weka. Glob J Comput Sci Technol

24.

Kumar NK, Sikamani KT (2020) Prediction of chronic and infectious diseases using machine learning classifiers-a systematic approach. Int J Intell Syst 13(4):11–20

25.

Kumar NK, Sindhu GS, Prashanthi DK, Sulthana AS (2020) Analysis and prediction of cardio vascular disease using machine learning classifiers. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, pp. 15–21

26.

Kumar R, Rani P (2020) Comparative analysis of decision support system for heart disease. Adv Math Sci J 9(6):3349–3356CrossRef

27.

Latha CBC, Jeeva SC (2019) Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inform Med Unlocked 16:100203CrossRef

28.

Louridi N, Amar M, El Ouahidi B (2019) Identification of cardiovascular diseases using machine learning. In: 2019 7th Mediterranean congress of telecommunications (CMT). IEEE, pp 1–6

29.

Mienye ID, Sun Y, Wang Z (2020) An improved ensemble learning approach for the prediction of heart disease risk. Inform Med Unlocked 20:100402CrossRef

30.

Mishra J, Tarar S (2020) Chronic disease prediction using deep learning. In: Singh M, Gupta PK, Tyagi V, Flusser J, Ören T, Valentino G (eds) ICACDS 2020, vol 1244. CCIS. Springer, Singapore, pp 201–211. https://doi.org/10.1007/978-981-15-6634-9_19CrossRef

31.

Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542–81554CrossRef

32.

Mourao-Miranda J, Bokde AL, Born C, Hampel H, Stetter M (2005) Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage 28(4):980–995CrossRef

33.

Obasi T, Shafiq MO (2019)Towards comparing and using machine learning techniques for detecting and predicting heart attack and diseases. In: 2019 IEEE International Conference on Big Data (Big Data). IEEE, pp 2393–2402

34.

Oh MS, Jeong MH (2020) Sex differences in cardiovascular disease risk factors among Korean adults. Korean J Med 95(4):266–275CrossRef

35.

Pasha SJ, Mohamed ES (2020) Novel feature reduction (NFR) model with machine learning and data mining algorithms for effective disease risk prediction. IEEE Access 8:184087–184108CrossRef

36.

Pavithra V, Jayalakshmi V (2021) Hybrid feature selection technique for prediction of cardiovascular diseases. In: Materials Today: Proceedings

37.

Perumal R, Kaladevi A (2020) Early prediction of coronary heart disease from Cleveland dataset using machine learning techniques. Int J Adv Sci Technol 29:4225–4234

38.

Pouriyeh S, Vahid S, Sannino G, De Pietro G, Arabnia H, Gutierrez J (2017) A comprehensive investigation and comparison of machine learning techniques in the domain of heart disease. In: 2017 IEEE Symposium on Computers and Communications (ISCC). IEEE, pp 204–207

39.

Ramalingam V, Dandapath A, Raja MK (2018) Heart disease prediction using machine learning techniques: a survey. Int J Eng Technol 7(2.8):684–687

40.

Rashmi G, Kumar U (2019) Machine learning methods for heart disease prediction. Int J Eng Adv Technol 8(5S):220–223CrossRef

41.

Reddy KVV, Elamvazuthi I, Aziz AA, Paramasivam S, Chua HN, Pranavanand S (2021) Heart disease risk prediction using machine learning classifiers with attribute evaluators. Appl Sci 11(18):8352CrossRef

42.

Saqlain SM et al (2019) Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines. Knowl Inf Syst 58(1):139–167CrossRef

43.

Shamrat FJM, Raihan MA, Rahman AS, Mahmud I, Akter R et al (2020) An analysis on breast disease prediction using machine learning approaches. Int J Sci Technol Res 9(02):2450–2455

44.

Sharma H, Rizvi M (2017) Prediction of heart disease using machine learning algorithms: a survey. Int J Recent Innov Trends Comput Commun 5(8):99–104

45.

Sharma S, Parmar M (2020) Heart diseases prediction using deep learning neural network model. Int J Innov Technol Explor Eng 9(3):124–137

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 2020

47.

Tougui I, Jilbab A, El Mhamdi J (2020) Heart disease classification using data mining tools and machine learning techniques. Health Technol 10(5):1137–1144CrossRef

48.

Trevisan C, Sergi G, Maggi S (2020) Gender differences in brain-heart connection. Brain Heart Dyn. 937–951

49.

Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N (2017) Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE 12(4):e0174944CrossRef

50.

Yadav DC, Pal S (2020) Prediction of heart disease using feature selection and random forest ensemble method. Int J Pharm Res 12(4):56–66

51.

Zhao D (2021) Epidemiological features of cardiovascular disease in Asia. JACC: Asia 1(1):1–13

Titel: Prediction of Heart Disease and Heart Failure Using Ensemble Machine Learning Models
verfasst von: Abdullah Al Maruf
Aditi Golder
Abdullah Al Numan
Md. Mahmudul Haque
Zeyar Aung
Verlag: Springer Nature Singapore
Buch: Intelligent Systems
Print ISBN: 978-981-9939-31-2

Electronic ISBN: 978-981-9939-32-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-3932-9_41

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