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Sumit Mali, Aashish Gaike, Sanskriti Sharma, Shruti Tomake, Aishwarya Sapre
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- Assistant Professor, Student, Student, Student, Student,NBN Sinhgad School of Engineering, NBN Sinhgad School of Engineering, NBN Sinhgad School of Engineering, NBN Sinhgad School of Engineering, NBN Sinhgad School of Engineering,Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra,India, India, India, India, India
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Abstract
nIt is challenging to estimate the likelihood of complex chronic disease while treating conditions like heart failure. The application of machine learning (ML), an area of artificial intelligence (AI), in cardiovascular care is growing quickly. In essence, it defines how computers classify and understand data, or choose a task with or without human intervention. The theoretical underpinnings of machine learning (ML) are models that accept input data (such as images or text) and forecast results using a combination of mathematical optimisation and statistical analysis (e.g., favourable, unfavourable, or neutral). Aiming to deliver results with an accuracy of 97.5%, the Heart Failure Prediction Model is based on machine learning algorithms like Random Forest and Logistic Regression. It includes sections that list the symptoms of cardiovascular diseases, offer preventative measures, and allow users to schedule doctor appointments. In order to create a prediction model based on machine learning, this document tries to capture the system needs and features. By using that, we can estimate the risk of heart disease. Heart disease sym ptoms can be reduced and understood with the help of machine learning (ML). choosing particular qualities. Additionally, the website is a subscription-based model with a unique attribute that includes several courses at various price points that are offered on the website related to their everyday activities. It consists of a yoga instructor and a nutritionist who will advise the customer on correct diet plans and exercise regimens.
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Keywords: ML (Machine Learning), AI (Artificial Intelligence), Random forest, Logistic Regression, heart failure prediction models,
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Computer Science Languages(ijcsl)]
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nSumit Mali, Aashish Gaike, Sanskriti Sharma, Shruti Tomake, Aishwarya Sapre Predicting and Prohibiting the Risk of Heart Failure Using Machine Learning ijcsl July 6, 2023; 01:15-20
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Full Text
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Browse Figures
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References
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1. Singh A, Kumar R. Heart disease prediction using machine learning algorithms. In 2020 international conference on electrical and electronics engineering (ICE3) 2020 Feb 14 (pp. 452–457). IEEE.
2. Anuja Bhosale, Gayatri Gadas, et al. Detection of Phishing Websites Using Machine Learning. International Journal of Advanced Research in Computer and Communication Engineering. 2022;11(6):490–494.
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6. Greenberg B, Brann A, Campagnari C, Adler E, Yagil A. Machine Learning Applications in Heart Failure Disease Management: Hype or Hope?. Current Treatment Options in Cardiovascular Medicine. 2021 Jun;23(6):35.
7. Ho IM, Cheong KY, Weldon A. Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques. Plos one. 2021 Apr 2;16(4):e0249423.
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10. learning machine. Random Forest. Learning Machine Learning. 2023. Available from: https://learningmachine.hashnode.dev/random-forest
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| Volume | 01 |
| Issue | 01 |
| Received | June 7, 2023 |
| Accepted | June 20, 2023 |
| Published | July 6, 2023 |
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