Optimizing Heart Disease Prediction: Comparative Analysis of Machine Learning Algorithm for Early Detection

Year : 2024 | Volume :02 | Issue : 01 | Page : –
By

Dr. Purushotam naidu K

K. Roshini

J. Sravanthi

T. Kanchana Rekha

N. Sirisha

M. Thanushya

  1. Assistant Professor Dept. of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
  2. Student Dept. of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
  3. student Dept. of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
  4. Student Dept. of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
  5. Student Dept. of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
  6. Student Dept. 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 endeavour. 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 last 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 (ML), 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)]

How to cite this article: Dr. Purushotam naidu K, K. Roshini, J. Sravanthi, T. Kanchana Rekha, N. Sirisha, M. Thanushya. Optimizing Heart Disease Prediction: Comparative Analysis of Machine Learning Algorithm for Early Detection. International Journal of Computer Science Languages. 2024; 02(01):-.
How to cite this URL: Dr. Purushotam naidu K, K. Roshini, J. Sravanthi, T. Kanchana Rekha, N. Sirisha, M. Thanushya. Optimizing Heart Disease Prediction: Comparative Analysis of Machine Learning Algorithm for Early Detection. International Journal of Computer Science Languages. 2024; 02(01):-. Available from: https://journals.stmjournals.com/ijcsl/article=2024/view=145661




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Regular Issue Subscription Review Article
Volume 02
Issue 01
Received March 28, 2024
Accepted April 8, 2024
Published May 9, 2024