Harnessing machine learning technique for improved detection and classification of heart failure
DOI:
https://doi.org/10.56294/dm2024.356Keywords:
Machine learning, Echocardiography, Decision trees, artificial neural networksAbstract
Artificial Intelligence (AI) performs exercises recently performed by people utilizing AI and profound learning, Right now simulated intelligence is changing cardiovascular medication identifying problems, therapeutics, risk appraisals, clinical consideration, and medication advancement. The death rates in medical clinics for patients with cardiovascular breakdown display a scope of 10.6% at 30 days, 23.0% at 1 year, and 43.3% at 5 years. Cardiovascular breakdown (HF) patients need customized restorative and careful treatment, in this way early finding is pivotal. The 85% precise Brain Organization (NN) archetypal made this conceivable. By applying our calculation, simulated intelligence can assist with examining crude cardiovascular imaging information from echocardiography, processed tomography, and heart attractive reverberation imaging and EKG accounts. Unpleasant Sets (RS) and strategic relapse (LR) choice trees to analyze congestive cardiovascular breakdown and computerized reasoning to identify future impermanence and destabilization incidents have further developed cardiac illness results. This examination inspects how computer- based intelligence has changed pretty much every area of HF determination, avoidance, and the executives
References
[1] Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review.
1958; 65: 386–408.
[2] Koza JR, Bennett FH, Andre D, Keane MA. Automated Design of both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. Artificial Intelligence in Design ’96. 1996; 7: 151–170.
[3] Brownlee J. 4 Types of Classification Tasks in Machine Learning. Machine Learning Mastery. 2021. Available at: https://machinelearningmastery.com/types-of-classification-in-machine-learning/ (Accessed: 23 July 2021).
[4] Brownlee J. A Gentle Introduction to Imbalanced Classification. 2019. Available at: https://machinelearningmastery.com/what-is-imbalanced-classification/ (Accessed: 16 August 2021).
[5] Romiti S, Vinciguerra M, Saade W, Anso Cortajarena I, Greco E. Artificial Intelligence (AI) and cardiovascular diseases: an Unexpected Alliance. Cardiology Research and Practice. 2020; 2020: 1–8.
[6] Zghyer F, Yadav S, Elshazly MB. Artificial Intelligence and Machine Learning. Precision Medicine in Cardiovascular Disease Prevention. 2021; 18: 133–148.
[7] Frankenfield J. Artificial Neural Network (ANN). 2021. Available at: https://www.investopedia.com/terms/a/artificial-neural-net works-ann.asp (Accessed: 11 May 2021).
[8] Valueva MV, Nagornov NN, Lyakhov PA, Valuev GV, Chervyakov NI. Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation.2020; 177: 232–243.
[9] Dupond S. A thorough review on the current advance of neural network structures. Annual Reviews in Control. 2019; 14: 200–230.
[10] Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. The Journal of the American Medical Association. 2016; 316: 2402.
[11] Ski CF, Thompson DR, Brunner‐La Rocca H. Putting AI at the centre of heart failure care. ESC Heart Failure. 2020; 7: 3257–3258.
[12] Yan Y, Zhang JW, Zang GY, Pu J. The primary use of artificial intelligence in cardiovascular diseases: what kind of potential role does artificial intelligence play in future medicine. Journal of Geriatric Cardiology. 2019; 16: 585–591.
[13] Dawes TJW, de Marvao A, Shi W, Fletcher T, Watson GMJ, Wharton J, et al. Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: a Cardiac MR Imaging Study. Radiology. 2017;283: 381–390.
[14] What is the mortality rate for heart failure? 2021. Available at: https://www.medscape.com/answers/163062-86190/what-is-the-mortality-rate-for-heart-failure (Accessed: 11 May 2021).
[15] Bhardwaj, S., Jain, S., Trivedi, N.K., Kumar, A., Tiwari, R.G. (2022). Intelligent Heart Disease Prediction System Using Data Mining Modelling Techniques. In: Kumar, R., Ahn, C.W., Sharma, T.K., Verma, O.P., Agarwal, A. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 425. Springer, Singapore. https://doi.org/10.1007/978-981-19-0707-4_79
[16] N. K. Trivedi, V. Gautam, H. Sharma, A. Anand and S. Agarwal, "Diabetes Prediction using Different Machine Learning Techniques," 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2022, pp. 2173-2177, doi: 10.1109/ICACITE53722.2022.9823640.
[17] Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial Intelligence in Surgery: Promises and Perils. Annals of Surgery. 2018; 268: 70–76.
[18] Hsieh P. AI In Medicine: Rise of The Machines. (n.d.). 2017. Available at: https://www.forbes.com/sites/paulhsieh/2017/04/30/ai-in-medicine-rise-of-the-machines/?sh=60c97063abb0 (Accessed: 14 July 2021).
[19] Gharehchopogh FS, Khalifelu ZA. Neural Network application in diagnosis of patient: a case study. International Conference on Computer Networks and Information Technology. 2011; 245–249.
[20] HealthITAnalytics. Artificial Intelligence May Accelerate Heart Failure Diagnosis. 2020. Available at: https://healthitanalytics.com/news/artificial-intelligence-may-accelerate-heart-failure-diagnosis (Accessed: 11 May 2021).
[21] Kwon J, Kim K, Eisen HJ, Cho Y, Jeon K, Lee SY, et al. Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features. European Heart Journal - Digital Health. 2021; 2: 106–116.
[22] Yao X, Rushlow DR, Inselman JW, McCoy RG, Thacher TD, Behnken EM, et al. Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nature Medicine. 2021; 27: 815–819.
[23] Luo X, Fang F, So H, Liu C, Yam M, Lee AP. Automated left heart chamber volumetric assessment using three-dimensional echocardiography in Chinese adolescents. Echo Research and Practice. 2017; 4: 53–61.
[24] Son C, Kim Y, Kim H, Park H, Kim M. Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches. Journal of Biomedical Informatics. 2012; 45: 999–1008.
[25] Masetic Z, Subasi A. Congestive heart failure detection using random forest classifier. Computer Methods and Programs in Biomedicine. 2016; 130: 54–64.
[26] Wu J, Roy J, Stewart WF. Prediction Modeling Using EHR Data. Medical Care. 2010; 48: S106–S113.
[27] Jovic A, Bogunovic N. Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features. Artificial Intelligence in Medicine. 2011; 51: 175–186.
[28] Francesco B, Maria Grazia B, Emanuele G, Valentina F, Sara C, Chiara F, et al. Linear and Nonlinear Heart Rate Variability Indexes in Clinical Practice. Computational and Mathematical Methods in Medicine. 2012; 2012: 1–5.
[29] Hussain L, Awan IA, Aziz W, Saeed S, Ali A, Zeeshan F, et al. Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques. BioMed Research International. 2020; 2020: 1–19.
[30] Zheng Y, Guo X, Qin J, Xiao S. Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics. Computer Methods and Programs in Biomedicine. 2015; 122: 372–383.
[31] Choi D, Park JJ, Ali T, Lee S. Artificial intelligence for the diagnosis of heart failure. Npj Digital Medicine. 2020; 3: 54.
[32] Matsumoto T, Kodera S, Shinohara H, Kiyosue A, Higashikuni Y, Akazawa H, et al. Diagnosing heart failure from chest X-ray images using deep learning. European Heart Journal. 2020; 61: 781–786.
[33] Bachtiger P, Plymen CM, Pabari PA, Howard JP, Whinnett ZI, Opoku F, et al. Artificial Intelligence, Data Sensors, and Interconnectivity: Future Opportunities for Heart Failure. Cardiac Failure Review. 2020; 6: e11.
[34] Adamson PB, Abraham WT, Aaron M, Aranda JM, Bourge RC, Smith A, et al. CHAMPION Trial Rationale and Design: The Long-Term Safety and Clinical Efficacy of a Wireless Pulmonary Artery Pressure Monitoring System. Journal of Cardiac Failure.
2011; 17: 3–10.
[35] Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P, Jouni H, et al. Artificial Intelligence in Cardiology: Present and Future. Mayo Clinic Proceedings. 2020; 95: 1015–1039.
[36] Li C, Hu X, Zhang L. The IoT-based heart disease monitoring system for pervasive healthcare service. Procedia Computer Science. 2017; 112: 2328–2334.
[37] N. K. Trivedi, R. G. Tiwari, A. Anand, V. Gautam, D. Witarsyah and A. Misra, "Application of Machine Learning for Diagnosis of Liver Cancer," 2022 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS), Bandung, Indonesia, 2022, pp. 1-5, doi: 10.1109/ICADEIS56544.2022.10037379.
[38] Boehmer JP, Hariharan R, Devecchi FG, Smith AL, Molon G, Capucci A, et al. A Multisensor Algorithm Predicts Heart Failure
Events in Patients with Implanted Devices. JACC: Heart Failure. 2017; 5: 216–225.
[39] Ahmed FZ, Taylor JK, Green C, Moore L, Goode A, Black P, et al. Triage‐HF Plus: a novel device‐based remote monitoring pathway to identify worsening heart failure. ESC Heart Failure. 2020; 7: 108–117.
[40] Stehlik J, Schmalfuss C, Bozkurt B, Nativi-Nicolau J, Wegerich S, Rose K, et al. Continuous wearable monitoring analytics predict heart failure decompensation: the link-hf multi-center study. Journal of the American College of Cardiology. 2018; 71: A646.
[41] Smith GB, Prytherch DR, Schmidt PE, Featherstone PI, Higgins B. A review, and performance evaluation, of single parameter “track and trigger” systems. Resuscitation. 2008; 79: 11–21.
[42] Kwon J, Lee Y, Lee Y, Lee S, Park J. An Algorithm Based on Deep Learning for Predicting in‐Hospital Cardiac Arrest. Journal of the American Heart Association. 2018; 7: e008678.
[43] Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP. Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards. Critical Care Medicine. 2016; 44: 368–374.
[44] Cikes M, Sanchez-Martinez S, Claggett B, Duchateau N, Piella G, Butakoff C, et al. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. European Journal of Heart Failure. 2019; 21: 74–85.
[45] Hedman ÅK, Hage C, Sharma A, Brosnan MJ, Buckbinder L, Gan L, et al. Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning. Heart. 2020; 106: 342–349.
[46] Przewlocka-Kosmala M, Marwick TH, Dabrowski A, Kosmala W. Contribution of Cardiovascular Reserve to Prognostic Categories of Heart Failure with Preserved Ejection Fraction: a Classification Based on Machine Learning. Journal of the American Society of Echocardiography. 2019; 32: 604–615.e6.
[47] Austin PC, Tu JV, Ho JE, Levy D, Lee DS. Using methods from the data mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. Journal of Clinical Epidemiology. 2013; 66: 398–407.
[48] Smith A. Smartphone Ownership 2013. 2013. Available at: https://www.pewresearch.org/internet/2013/06/05/smartphone-ownership-2013/ (Accessed: 25 May 2021).
[49] Mobile Health: Tools, Benefits, and Applications. 2020. Available at: https://healthinformatics.uic.edu/blog/mobile-health/ (Accessed: 25 May 2021).
[50] Cajita MI, Gleason KT, Han H. A Systematic Review of mHealth- Based Heart Failure Interventions. Journal of Cardiovascular Nursing. 2016; 31: E10–E22.
[51] León MP. The impact of gentrification policies on urban development. Gentrification 2023;1:4–4. https://doi.org/10.62486/gen20234.
[52] Madariaga FJD. Pedagogical model for the integration of ICTs into teaching practices in official educational institutions in rural Monteria. Multidisciplinar (Montevideo) 2024;2:105–105. https://doi.org/10.62486/agmu2024%p.
[53] Muthusundari M, Velpoorani A, Kusuma SV, L T, Rohini O k. Optical character recognition system using artificial intelligence. LatIA 2024;2:98–98. https://doi.org/10.62486/latia202498.
[54] Navarro WS, Duque NEA, Ramirez FMB, Chaparro AMT. Strategic Analysis from a consulting context for the Super Kinder School Institution. Management (Montevideo) 2024;2:30–30. https://doi.org/10.62486/agma202430.
[55] Pérez GAJ, Cruz JMH de la. Applications of Artificial Intelligence in Contemporary Sociology. LatIA 2024;2:12–12. https://doi.org/10.62486/latia202412.
[56] TR, R.., Lilhore, U. K., M, P.., Simaiya, S. ., Kaur, . A. ., & Hamdi, M. . (2022). PREDICTIVE ANALYSIS OF HEART DISEASES WITH MACHINE LEARNING APPROACHES. Malaysian Journal of Computer Science, 132–148. https://doi.org/10.22452/mjcs.sp2022no1.10 [52] Chaverneff F. The Role of AI in Managing Cardiovascular Disease and Diabetes. 2020 (Accessed: 25 May 2021).
[57] Steinhubl SR, Muse ED, Topol EJ. The emerging field of mobile health. Science Translational Medicine. 2015; 7: 283rv3–283rv3.
[58] Indraratna P, Tardo D, Yu J, Delbaere K, Brodie M, Lovell N, et al. MHealth interventions in the management of heart failure, ischaemic heart disease and hypertension: a systematic review and meta-analysis of randomised controlled trials. European Heart Journal. 2020; 41
Published
Issue
Section
License
Copyright (c) 2024 Subba Rao P, Venubabu Rachapudi (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
