Abstract

This study centers around the improvement of an artificial intelligence- and computerized reasoning based heart sickness determination framework. We exhibit how AI can help with foreseeing whether an  individual will get cardiovascular infection. In this review, a Python-based application for medical care  research is created since it is more reliable and helps track and lay out many kinds of well-being  observing applications. We show information handling, which incorporates working with all out factors  and changing over unmitigated sections. This paper covers the three significant phases of utilization  improvement: gathering information bases, applying calculated relapse, and evaluating the dataset’s  properties. A random forest order framework is being created to more readily analyze heart issues.  This application, which is highly reliable in light of the fact that it has around 83% precision rate across  preparing information, requires information examination. The random forest classifier method is next  examined, including the preliminaries and discoveries, which give further developed correctness to  investigate determination. The paper finishes up with targets, constraints, and exploration commitments. 

Keyworde: Artificial intelligence, heart disease detection system, machine learning, predictive analytics, random forest classifier algorithm

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Refrences:

1. Libby P, Bonow RO, Mann DL, Zipes DP. Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine. 8th edition. New York, NY, USA: Elsevier; 2007..
2. Rosebrock A. Deep Learning for Computer Vision with Python. Philadelphia, PA, USA: PyImageSearch; 2018.
3. Chang V, Bhavani VR, Xu AQ, Hossain MA. An artificial intelligence model for heart disease detection using machine learning algorithms. Healthc Analyt. 2022; 2: 100016.
4. Lesser LI, Behal R. Change in glycemic control for patients enrolled in a membership-based primary care program: longitudinal observational study. JMIR Diabetes. 2021; 6 (2): e27453.
5. Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM,  et al. Heart disease and stroke statistics—2019 update: a report from the American Heart  Association. Circulation. 2019; 139 (10): e56–e528. 
6. Ayano YM, Schwenker F, Dufera BD, Debelee TG. Interpretable machine learning techniques in ECG-based heart disease classification: a systematic review. Diagnostics. 2022; 13 (1): 111.
7. Barot P. Why Use Python in Healthcare Applications? Singapore: BoTree Technologies; 2020.
8. Zheng Y, Chen Z, Huang S, Zhang N, Wang Y, Hong S, Chan JSK, Chen K-Y, Xia Y, Zhang Y, Lip GYH, Qin J, Tse G, Liu T. Machine learning in cardio-oncology: new insights from an  emerging discipline. Rev Cardiovasc Med. 2023; 24 (10): 296.
9. Zhao Y, Qiao Z, Xiao C, Glass L, Sun J. Pyhealth: a python library for health predictive models. arXiv preprint arXiv:2101.04209. 2021. Available at https://arxiv.org/abs/2101.04209
10. Lasser J, Manik D, Silbersdorff A, Säfken B, Kneib T. Introductory data science across disciplines, using Python, case studies, and industry consulting projects. Teach Statistics. 2021; 43: S190–S200.
11. Calix RA, Singh SB, Chen T, Zhang D, Tu M. Cyber security tool kit (CyberSecTK): a Python library for machine learning and cyber security. Information. 2020; 11 (2): 100.
12. Pala SK. Implementing master data management on healthcare data tools like Data Flux, MDM  Informatica and Python. Int J Transcontinent Discov. 2023; 10 (1): 35–41. 
13. Ahsan MM, Siddique Z. Machine learning-based heart disease diagnosis: a systematic literature review. Artif Intell Med. 2022; 128: 102289.
14. Navlani A. Understanding random forests classifiers in Python. [Online]. 2018. Datacamp. Available at https://www.datacamp.com/community/tutorials/random-forests-classifier-python.
15. Lemenkova P. Python libraries matplotlib, seaborn and pandas for visualization geo-spatial datasets generated by QGIS. Analele stiintifice ale Universitatii Alexandru Ioan Cuza din Iasi-seria  Geografie. 2020; 64 (1): 13–32. 
16. Larsen AH, Mortensen JJ, Blomqvist J, Castelli IE, Christensen R, Duła M, Friis J, et al. The atomic simulation environment—a Python library for working with atoms. J Phys Condens Matter. 2017; 29 (27): 273002.
17. May RM, Goebbert KH, Thielen JE, Leeman JR, Camron MD, Bruick Z, Bruning EC, Manser RP, Arms SC, Marsh PT. MetPy: a meteorological Python library for data analysis and visualization. Bull Am Meteorol Soc. 2022; 103 (10): E2273–E2284.
18. Chen Y, Zheng W, Li W, Huang Y. Large group activity security risk assessment and risk early warning based on random forest algorithm. Pattern Recogn Lett. 2021; 144: 1–5. 19. Dogan MV, Grumbach IM, Michaelson JJ, Philibert RA. Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study. PLoS One. 2018; 13 (1):  e0190549. 
19. Mishra P. Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-Based Libraries, Extensions, and Frameworks. Berkeley, CA, USA: Apress, 2022. 21. Tauzin G, Lupo U, Tunstall L, Pérez JB, Caorsi A, Medina-Mardones AM, Dassatti A, Hess K. giotto-tda: a topological data analysis toolkit for machine learning and data exploration. J Mach Learn Res. 2021; 22 (1): 1834–1839. 
20. Peters B, Haber E, Granek J. Neural networks for geophysicists and their application to seismic data interpretation. The Leading Edge. 2019; 38 (7): 534–540.
21. Mehmood F, Rashidkayani HU, Hussain F. Chronic diseases modelling–python environment. FUUAST J Biol. 2020; 10 (1): 31–38.