Optimized Adaptive Neuro-Fuzzy Inference Model for Early Identification of Premature Heart Attacks

References

[1] Adler, E. D., Voors, A. A., Klein, L., Macheret, F., Braun, O. O. & Urey, M. A. (2020). Improving risk prediction in heart failure using machine learning. Eur J. Heart Fail 22(1), 139–147.

[2] Akalya, A. & Swedha, V.. (2024). Heart Attack Prediction Using Big Data Analytics. 10.1007/978-3-031-69986-3_22.

[3] Ali, M. L., Sadi, M. S., & Goni, M. O. (2024). Diagnosis of heart diseases: A fuzzy-logic-based approach. PloS one, 19(2), e0293112. https://doi.org/10.1371/journal.pone.0293112

[4] Balamurugan, R., Ratheesh S. & Venila Y.M. (2022). Classification of heart disease using adaptive Harris hawk optimization-based clustering algorithm and enhanced deep genetic algorithm. Soft Comput. 2022;(26)2357–2373.

[5] Bamanga, M.A. Ahmadu, S.A., Yusuf, M.M. & Sajoh, D. I. (2021). Ensemble model for Heart Disease Prediction. Science Progress and Research, 1(4), 268-280. doi:10.52152/spr/2021.145

[6] Bede, B. (2013). Mathematics of Fuzzy Sets and Fuzzy Logic. Springer: Berlin, Germany, 2013.

[7] Benjamin, E.J., Virani, S.S., Callaway, C.W., Chamberlain, A.M., Chang, A.R., S. Cheng, S.E. Chiuve, M. Cushman, F.N. Delling, R.J.C. Deo, (2018). Heart disease and stroke statistics-2018 update: a report from the American Heart Association, 137. 123-134.

[8] Chee, K.J. &Ramli D. A. (2022). Electrocardiogram Biometrics Using Transformer’s Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification. Sensors. 22:3446.

[9] Dipti, I. (2022). Artificial intelligence in cardiology. Trends in Cardiovascular Medicine, 32(1).34-41,ISSN 1050-1738. https://doi.org/10.1016/j.tcm.2020.11.007.

[10] Diwakar, M., Tripathi, A., Joshi, K., Memoria, M., Singh, P. &kumar, N. (2021) Latest trends on heart disease prediction using machine learning and image fusion. Materials Today: Proceedings 37, 3213-3218

[11] Du, N., Cao Q., Yu L., Liu N., Zhong E., Liu Z., Shen Y. & Chen K. (2020). FM-ECG: A fine-grained multi-label framework for ECG image classification. Inf. Sci.549:164–177.

[12] Dubey, A. K., Sinhal, A. K., & Sharma, R. (2022). An Improved Auto Categorical PSO with ML for Heart Disease Prediction. Engineering, Technology & Applied Science Research, 12(3), 8567–8573. https://doi.org/10.48084/etasr.4854

[13] Jawalkar, A.P., Swetcha, P. & Manasvi, N. (2023). Early prediction of heart disease with data analysis using supervised learning with stochastic gradient boosting. J. Eng. Appl. Sci. 70, 122. https://doi.org/10.1186/s44147-023-00280-y

[14] Jayesh B. A. (2018). The Artificial Neural Networks handbook: Part 1, https://www.datasciencecentral.com/the-artificial-neural-networks-handbook-part-1/

[15] Jorge, L. (2018). Artificial Neural Networks (Deep Learning). https://medium.com/@jorgesleonel/artificial-neural-networks-deep-learning-21201054ca29

[16] Kilic, A. (2020). Artificial Intelligence and Machine Learning in Cardiovascular Health Care. Annal of Thoracic Surgeon, 109, 1323–1329.

[17] Kumar, M.N. &Koushik, K. (2018). Deepak, Prediction of Heart Diseases Using Data Mining and Machine Learning Algorithms and Tools, DOI.

[18] Kumar, P., Lai, S., Wong, J.K., Mohd, N., Rowshon, M., Afan, H., Ali N.A., Sherif, M., Sefelnasr, A., & El-Shafie, A. (2020). Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models. Sustainability. 12. 4359. 10.3390/su12114359.

[19] Lee, T., Ullah, A. & Wang, R. (2020). Bootstrap Aggregating and Random Forest. 10.1007/978-3-030-31150-6_13.

[20] Maleki, V.S. & Forouzanfar, M. (2024). The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering (Basel, Switzerland), 11(4), 337. https://doi.org/10.3390/bioengineering11040337

[21] Migliaccio, G. M., Padulo, J., & Russo, L. (2024). The Impact of Wearable Technologies on Marginal Gains in Sports Performance: An Integrative Overview on Advances in Sports, Exercise, and Health. Applied Sciences, 14(15), 6649. https://doi.org/10.3390/app14156649

[22] Mohammed, A. & Kora, R. (2023). A Comprehensive Review on Ensemble Deep Learning: Opportunities and Challenges. Journal of King Saud University - Computer and Information Sciences. 35. 10.1016/j.jksuci.2023.01.014.

[23] Negassa, A., Ahmed, S., Zolty, R. & Patel, S.R. (2021). Prediction model using machine learning for mortality in patients with heart failure. Am J Cardiol 153:86–93.

[24] Pavithra M., M. (2022). Effective Heart Disease Prediction Systems Using Data Mining Techniques.Annalsofrscb.ro.Https://www.annalsofrscb.ro/index.php/journal/article/view/2172

[25] Pires, I. M., Marques, G., Garcia, N.M. &Ponciano V. (2020). Machine learning for the evaluation of thepresence of heart disease. ProcediaComputSci 177:432–437.

[26] Rahim, A., Rasheed, Y., Azam, F., Anwar, M.W., Rahim, M.A. &Muzaffar, A.W. (2021). An integrated machine learning framework for effective prediction of cardiovascular diseases. IEEE Access  9, 106575–106588.

[27] Rani, P., Kumar, R., Ahmed, N. & Jain, A. (2021) A decision support system for heart disease prediction based upon machine learning. Journal of Reliable Intelligent Environments 7, 263-275.

[28] Raschka S, Patterson J, & Nolet C. (2020) Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence. Information. 2020; 11(4):193. https://doi.org/10.3390/info11040193

[29] Reddy G.T., Reddy M., Lakshmanna K., Rajput D.S., Kaluri R., Srivastava G. (2020). Hybrid geneticalgorithm and a fuzzy logic classifier for heart disease diagnosis. Evol. Intell. 2020;13:185–196. doi: 10.1007/s12065-019-00327-1. 

[30] Rehman, I, Rehman, A. (2021). Anatomy Thorax, Heart. In: StatPearls Treasure Is land Stat Pearls Publishing; Available:https://www.ncbi.nlm.nih.gov/books/NBK470256/ (accessed Jan. 2024)

[31] Roy S.S., Ahmed M., Akhand M.A.H. Noisy image classification using hybrid deep learning methods. J. Inf. Commun. Technol. 2018;17:233–269.

[32] Sagias, V. D., Zacharia, P., Tempeloudis, A., & Stergiou, C. (2024). Adaptive Neuro-Fuzzy Inference System-Based Predictive Modeling of Mechanical Properties in Additive Manufacturing. Machines, 12(8), 523. https://doi.org/10.3390/machines12080523.

[33] Saleh, H., Ibrahim, S., Alharbi, A., El-Sappagh, S. & Alkhalifah, T. (2022). Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis. Sensors. 22. 3707. 10.3390/s22103707.

[34] Sarabjeet, Singh, Satvir Singh & Vijay Kumar Banga (2019). Design of fuzzy logic system framework using evolutionary techniques, Soft computing2(4), 345.

[35] Shah, D., Patel S., & Bharti S.K.(2020). Heart Disease Prediction using Machine Learning Techniques. SN Comput. Sci. 2020;1:1–6.

[36] Shameer, K., Johnson, K. W., Glicksberg, B. S. (2018). Machine learning in cardiovascular medicine: are we there yet? Heart. 2018;104:1156-64.

[37] Siontis, K. C., Noseworthy, P. A., Attia, Z. I., & Friedman, P. A. (2021). Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nature reviews. Cardiology, 18(7), 465–478. https://doi.org/10.1038/s41569-020-00503-2

[38] Spotintelligence. (2023, August 9). Ensemble Learning. Retrieved from https://spotintelligence.com/2023/08/09/ensemble-learning/

[39] Stéfano, F. S., Zanetti, F. R.., Santos, C. L., Meyer, L. H., Bartnik G. R., Gouvêa, B W. & Nied, A. (2020). Electrical Insulator Fault Forecasting Based on aWavelet Neuro Fuzzy System. Energies MPDI, 23(2) 345.

[40] Srivastava, R., & Kumar, B. (2022). Design of Anfis based pacemaker controller having improved transient response and its FPGA implementation. Biomed. Signal Process. Control., 71, 103186.

[41] Tobore, I., Li, J., Yuhang, L., Al-Handarish, Y., Kandwal, A., Nie, Z., & Wang, L. (2019). Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR mHealth and uHealth, 7(8), e11966. https://doi.org/10.2196/11966

[42] Ullah, A., Anwar S.M., Bilal M., & Mehmood R.M. (2020). Classification of arrhythmia by using deep learning with 2-D ECG spectral image representation. Remote Sens. 2020;12:1685.

[43] Vidhya, K.A., & Shanmugalakshmi, R. (2020). Modified adaptive neuro-fuzzy inference system (M-ANFIS) based multi-disease analysis of healthcare Big Data. The Journal of Supercomputing, 76, 8657 - 8678.

[44] Vyas, S., Gupta, S., Bhargava, D., & Boddu, R. (2022). Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks. Journal of healthcare engineering, 2022, 9382322. https://doi.org/10.1155/2022/9382322 (Retraction published J Healthc Eng. 2023 May 24;2023:9816424.

[45] WHO, (2020) World Health Organization, news, WHO reveals leading causes of death and disability worldwide: 2000-2019. https://www.who.int/news/item/09-12-2020-who-revealsleading-causes-of-death-and-disability-worldwide-2000-2019

[46] Wolters, F. J., Segufa, R. A., Darweesh, S. K, (2022). Coronary heart disease, heart failure, and the risk of dementia: a systematic Med Records 2022;4(1):1-6

[47] WHO (2021). Cardiovascular disease fact sheet. 2021. https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).

[48] Wiener C, Fauci A, Braunwald E, Kasper D, Hauser S, Longo D, (2012). Harrisons Principles of Internal Medicine Self-Assessment and Board Review 18th Edition. 2012.

[49] W.Q. (2019). Learning from longitudinal data in electronic health records and genetic data to improve cardiovascular event prediction. Sci. Rep.1–10.

@article{1713801,
      author = {S. E. Dogo, E. J. Garba, Y. M. Malgwi, A. Danlami},
      title = {Optimized Adaptive Neuro-Fuzzy Inference Model for Early Identification of Premature Heart Attacks},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {7},
      pages = {1621-1631},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1713801.pdf},
      abstract = {Cardiovascular diseases (CVDs), particularly premature heart attacks, remain a leading cause of mortality worldwide, with increasing prevalence in developing countries such as Nigeria. Early detection is critical for effective prevention and clinical intervention. This study proposes an Ensemble Adaptive Neuro-Fuzzy Inference System (ANFIS) for the early diagnosis of premature heart attacks using clinically relevant patient data. The dataset was obtained from the Federal Medical Centre (FMC), Jalingo, Taraba State, and supplemented with benchmark datasets, comprising 918 patient records with 12 attributes related to cardiovascular health. Data preprocessing, feature encoding, and model training were conducted using Python-based machine learning and fuzzy logic libraries. The proposed ANFIS model was trained and evaluated using standard performance metrics. Experimental results demonstrate that the model achieved an overall accuracy of 92%, with high precision, recall, and F1-scores across both classes. The findings indicate that the ensemble ANFIS model is effective, robust, and suitable as an intelligent decision-support tool for early detection of premature heart attacks.},
      keywords = {Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System, Cardiovascular Diseases},
      month = {January},
  }