Smart Artificial Intelligence System for Heart Disease Prediction

Main Article Content

Dr. K Nagaiah

Abstract

Heart disease playing a vital role in human life, Early detection of heart-disease we can save humans lives and it remains a leading cause of mortality worldwide, making early and accurate prediction of heart disease a critical task for improving patient outcomes. Machine learning has shown great promise in this area, with various models being developed to predict heart disease based on a range of clinical and demographic features. However, there is a growing need for more efficient machine learning models that can accurately predict heart disease while minimizing computational costs, particularly in resource-constrained settings. This research paper proposes an efficient machine learning model for heart disease prediction that combines feature selection, model optimization, and interpretability techniques to achieve accurate predictions with reduced computational complexity. The proposed model utilizes a dataset of clinical and demographic features, such as age, sex, blood pressure, cholesterol levels, and other relevant risk factors, to train a machine learning model using a large real-world dataset. The proposed efficient machine learning model is evaluated on benchmark datasets and compared with other state-of-the-art models in terms of precision, Accuracy, Recall and F1- Score. The results demonstrate the model achieved by superior prediction performance to existing models. Proposed method accuracy increased by 4.8%.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
Dr. K Nagaiah , Tran., “Smart Artificial Intelligence System for Heart Disease Prediction”, IJEAT, vol. 13, no. 3, pp. 1–6, Feb. 2024, doi: 10.35940/ijeat.C4346.13030224.
Section
Articles

How to Cite

[1]
Dr. K Nagaiah , Tran., “Smart Artificial Intelligence System for Heart Disease Prediction”, IJEAT, vol. 13, no. 3, pp. 1–6, Feb. 2024, doi: 10.35940/ijeat.C4346.13030224.
Share |

References

Estes, C.; Anstee, Q.M.; Arias-Loste, M.T.; Bantel, H.; Bellentani, S.; Caballeria, J.; Colombo, M.; Craxi, A.; Crespo, J.; Day, C.P.; et al. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016–2030. J. Hepatol. 2018, 69, 896–904. https://doi.org/10.1016/j.jhep.2018.05.036

Dro ˙zd ˙z, K.; Nabrdalik, K.; Kwiendacz, H.; Hendel, M.; Olejarz, A.; Tomasik, A.; Bartman, W.; Nalepa, J.; Gumprecht, J.; Lip, G.Y.H. Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: A machine learning approach. Cardiovasc. Diabetol. 2022, 21, 240. https://doi.org/10.1186/s12933-022-01672-9

Murthy, H.S.N.; Meenakshi, M. Dimensionality reduction using neuro-genetic approach for early prediction of coronary heart disease. In Proceedings of the International Conference on Circuits, Communication, Control and Computing, Bangalore, India, 21–22 November 2014; pp. 329–332. https://doi.org/10.1109/CIMCA.2014.7057817

Benjamin, E.J.; Muntner, P.; Alonso, A.; Bittencourt, M.S.; Callaway, C.W.; Carson, A.P.; Chamberlain, A.M.; Chang, A.R.; Cheng, S.; Das, S.R.; et al. Heart disease and stroke statistics—2019 update: A report from the American heart association. Circulation 2019, 139, e56–e528.

Shorewala, V. Early detection of coronary heart disease using ensemble techniques. Inform. Med. Unlocked 2021, 26, 100655. https://doi.org/10.1016/j.imu.2021.100655

Mozaffarian, D.; Benjamin, E.J.; Go, A.S.; Arnett, D.K.; Blaha, M.J.; Cushman, M.; de Ferranti, S.; Després, J.-P.; Fullerton, H.J.; Howard, V.J.; et al. Heart disease and stroke statistics—2015 update: A report from the American Heart Association. Circulation 2015, 131, e29–e322. https://doi.org/10.1161/CIR.0000000000000152

Maiga, J.; Hungilo, G.G.; Pranowo. Comparison of Machine Learning Models in Prediction of Cardiovascular Disease Using Health Record Data. In Proceedings of the 2019 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), Jakarta, Indonesia, 24–25 October 2019; pp. 45–48. https://doi.org/10.1109/ICIMCIS48181.2019.8985205

Li, J.; Loerbroks, A.; Bosma, H.; Angerer, P. Work stress and cardiovascular disease: A life course perspective. J. Occup. Health 2016, 58, 216–219. https://doi.org/10.1539/joh.15-0326-OP

Purushottam; Saxena, K.; Sharma, R. Efficient Heart Disease Prediction System. Procedia Comput. Sci. 2016, 85, 962–969. https://doi.org/10.1016/j.procs.2016.05.288

Soni, J.; Ansari, U.; Sharma, D.; Soni, S. Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction. Int. J. Comput. Appl. 2011, 17, 43–48. https://doi.org/10.5120/2237-2860

Mohan, S.; Thirumalai, C.; Srivastava, G. Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques IEEE Access 2019, 7, 81542–81554. https://doi.org/10.1109/ACCESS.2019.2923707

Waigi, R.; Choudhary, S.; Fulzele, P.; Mishra, G. Predicting the risk of heart disease using advanced machine learning approach.Eur. J. Mol. Clin. Med. 2020, 7, 1638–1645.

Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. in medical imaging," ICRU News, pp. 7-16, 2017. https://doi.org/10.1023/A:1010933404324

Chen, T.; Guestrin, C. XGBoost: A scalable tree boosting system. In Proceedings of the KDD ’16: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; Association for Computing Machinery: New York, NY, USA, 2016; pp. 785–794. https://doi.org/10.1145/2939672.2939785

Gietzelt, M.; Wolf, K.-H.; Marschollek, M.; Haux, R. Performance comparison of accelerometer calibration algorithms based on 3D-ellipsoid fitting methods. Comput. Methods Programs Biomed. 2013, 111, 62–71. https://doi.org/10.1016/j.cmpb.2013.03.006

K, V.; Singaraju, J. Decision Support System for Congenital Heart Disease Diagnosis based on Signs and Symptoms using Neural Networks. Int. J. Comput. Appl. 2011, 19, 6–12 https://doi.org/10.5120/2368-3115

Narin, A.; Isler, Y.; Ozer, M. Early prediction of Paroxysmal Atrial Fibrillation using frequency domain measures of heart rate variability. In Proceedings of the 2016 Medical Technologies National Congress (TIPTEKNO), Antalya, Turkey, 27–29 October 2016. https://doi.org/10.1109/TIPTEKNO.2016.7863110

Shah, D.; Patel, S.; Bharti, S.K. Heart Disease Prediction using Machine Learning Techniques. SN Comput. Sci. 2020, 1, 345. https://doi.org/10.1007/s42979-020-00365-y

Alotaibi, F.S. Implementation of Machine Learning Model to Predict Heart Failure Disease. Int. J. Adv. Comput. Sci. Appl. 2019, 10, 261–268. https://doi.org/10.14569/IJACSA.2019.0100637

Hasan, N.; Bao, Y. Comparing different feature selection algorithms for cardiovascular disease prediction. Health Technol. 2020, 11, 49–62. https://doi.org/10.1007/s12553-020-00499-2

Ouf, S.; ElSeddawy, A.I.B. A proposed paradigm for intelligent heart disease prediction system using data mining techniques. J. Southwest Jiaotong Univ. 2021, 56, 220–240. https://doi.org/10.35741/issn.0258-2724.56.4.19.

Khan, I.H.; Mondal, M.R.H. Data-Driven Diagnosis of Heart Disease. Int. J. Comput. Appl. 2020, 176, 46–54. https://doi.org/10.5120/ijca2020920549

Young, L., York, J. R., & Kil Lee, B. (2023). Implications of Deep Compression with Complex Neural Networks. In International Journal of Soft Computing and Engineering (Vol. 13, Issue 3, pp. 1–6). https://doi.org/10.35940/ijsce.c3613.0713323

T M, N., & M Azzedine, Dr. M. (2023). Proofs of Beal’s Conjecture, Fermat’s Conjecture, Collatz Conjecture and Goldbach Conjecture. In Indian Journal of Advanced Mathematics (Vol. 3, Issue 1, pp. 1–7). https://doi.org/10.54105/ijam.a1137.043123

Kumar, P., & Rawat, S. (2019). Implementing Convolutional Neural Networks for Simple Image Classification. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 2, pp. 3616–3619). https://doi.org/10.35940/ijeat.b3279.129219

Behera, D. K., Das, M., & Swetanisha, S. (2019). A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 10809–10814). https://doi.org/10.35940/ijrte.d4362.118419

N.S, N., & A, S. (2020). Malware Detection using Deep Learning Methods. In International Journal of Innovative Science and Modern Engineering (Vol. 6, Issue 6, pp. 6–9). https://doi.org/10.35940/ijisme.f1218.046620

Most read articles by the same author(s)

1 2 3 4 5 6 > >>