Purushotam Naidu K.
K. Roshini
J. Sravanthi
T. Kanchana Rekha
N. Sirisha
M. Thanushya
- Assistant Professor Department of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
- Student Department of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
- Student Department of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
- Student Department of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
- Student Department of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
- Student Department of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
Abstract
The expanding realm of data analysis holds considerable importance in healthcare, particularly in the medical sector where forecasting heart disease is considered a complex endeavor. Early prediction of serious health conditions can be the determining factor between survival and fatality, with heart disease being one such critical health issue. Over the past decade, the main reason for death has been heart disease. Heart disorders come in many different forms, and they are often referred to as cardiovascular diseases. These can range from heart rhythm issues to birth abnormalities to illnesses of the blood vessels. For several decades, it has continued to be the leading cause of death worldwide. It is imperative to find a precise and trustworthy method for automating the task in order to detect the sickness early and manage it effectively. Machine learning, a prominent application of artificial intelligence, is making significant strides in various research domains. This study examines supervised learning models, including logistic regression, naïve Bayes, support vector machine, K-nearest neighbors, decision tree, random forest, and the ensemble technique XGBoost, offering a comparative analysis to identify the most effective algorithm. Results indicate that random forest achieves the highest accuracy at 90.16% compared to other algorithms.
Keywords: Classification accuracy, logistic regression, naïve bayes, support vector machine, K-nearest neighbor, decision tree, random forest
[This article belongs to International Journal of Computer Science Languages(ijcsl)]
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| Volume | 02 |
| Issue | 01 |
| Received | March 28, 2024 |
| Accepted | April 8, 2024 |
| Published | May 9, 2024 |