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Shivam Saini, Ujjwal Thakur, Shashwat Verma, Anand Sehgal
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- Student, Student, Student, Assistance Professor Department of computer science and engineering, The Northcap University, Department of computer science and engineering, The Northcap University, Department of computer science and engineering, The Northcap University, Department of computer science and engineering, The Northcap University Gurgaon, Haryana, Gurgaon, Haryana, Gurgaon, Haryana, Gurgaon, Haryana India, India, India, India
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Abstract
nThe present investigation looks at how well various machine learning algorithms predict cardiac disease. Since heart disease is one of the major causes of death worldwide, early detection and precise diagnosis are essential for managing and treating the condition. Our goal is to enhance diagnostic processes and improve patient outcomes by leveraging machine learning techniques.
Six widely-used machine learning algorithms are evaluated in this research paper. These algorithms were selected due to their established effectiveness in classification tasks. Numerous patient characteristics, including age, gender, blood pressure, cholesterol, and other pertinent medical data, are included in the dataset. To evaluate each algorithm’s performance, we separated the dataset into subsets for testing and training.
Our study’s primary goal is to evaluate each algorithm’s predictive power for heart disease. Various performance indicators are employed for both the training and testing datasets, such as accuracy, precision, recall, and F-measure. These metrics give a comprehensive view of each model’s predictive capabilities and how well they generalize.
This research has significant implications for the use of machine learning in healthcare. By identifying the strengths and weaknesses of different algorithms in predicting heart disease, we provide valuable insights that can help develop more reliable and accurate prediction models. This study not only broadens our understanding of machine learning applications in healthcare but also sets the stage for future research aimed at improving patient care through advanced predictive analytics.
Our study underscores the potential of machine learning to revolutionize diagnostic processes and improve outcomes for patients at risk of heart disease. By using these advanced algorithms, we can move towards more effective and personalized healthcare solutions.
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Keywords: Machine Learning, Heart Disease, Neural Network, Kaggle, Evaluation.
n[if 424 equals=”Regular Issue”][This article belongs to Trends in Mechanical Engineering & Technology(tmet)]
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References
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| Volume | ||
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | ||
| Received | May 9, 2024 | |
| Accepted | July 2, 2024 | |
| Published | July 17, 2024 |
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