Dr. Purushotam naidu K
K. Roshini
J. Sravanthi
T. Kanchana Rekha
N. Sirisha
M. Thanushya
- Assistant Professor Dept. of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
- Student Dept. of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
- student Dept. of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
- Student Dept. of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
- Student Dept. of Computer Science and Engineering (AI & ML), GVP College of Engineering for Women, Visakhapatnam Andhra Pradesh India
- 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)]
References
- Hossen MK. Heart disease prediction using machine learning techniques. American Journal of Computer Science and Technology. 2022;5(3):146-54.
- Heart Disease Prediction Using Machine Learning, Anupama Yadav, Levish Gediya1, Adnanuddin Kazi2, International Research Journal of Engineering and Technology (IRJET).
- Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande S, Singh P. Prediction of heart disease using a combination of machine learning and deep learning. Computational intelligence and neuroscience. 2021 Jul 1;2021.
- Malavika G, Rajathi N, Vanitha V, Parameswari P. Heart disease prediction using machine learning algorithms. Bioscience Biotechnology Research Communications. 2020;13(11):24-7.
- Biswas N, Ali MM, Rahaman MA, Islam M, Mia MR, Azam S, Ahmed K, Bui FM, Al-Zahrani FA, Moni MA. Machine Learning-Based Model to Predict Heart Disease in Early Stage Employing Different Feature Selection Techniques. BioMed Research International. 2023 May 2;2023.
- Nashif S, Raihan MR, Islam MR, Imam MH. Heart disease detection by using machine learning algorithms and a real-time cardiovascular health monitoring system. World Journal of Engineering and Technology. 2018 Sep 12;6(4):854-73.
- Shah D, Patel S, Bharti SK. Heart disease prediction using machine learning techniques. SN Computer Science. 2020 Nov;1(6):345.
- Ahmed I. A Study of Heart Disease Diagnosis Using Machine Learning and Data Mining.
- Shah D, Patel S, Bharti SK. Heart disease prediction using machine learning techniques. SN Computer Science. 2020 Nov;1(6):345.
- Jindal H, Agrawal S, Khera R, Jain R, Nagrath P. Heart disease prediction using machine learning algorithms. InIOP conference series: materials science and engineering 2021 (Vol. 1022, No. 1, p. 012072). IOP Publishing.
- Shah D, Patel S, Bharti SK. Heart disease prediction using machine learning techniques. SN Computer Science. 2020 Nov;1(6):345.
- Mahmoud WA, Aborizka M, Amer FA. Heart disease prediction using machine learning and data mining techniques: Application of framingham dataset. Turkish Journal of Computer and Mathematics Education (TURCOMAT). 2021 Nov 2;12(14):4864-70.
- Singh A, Kumar R. Heart disease prediction using machine learning algorithms. In2020 international conference on electrical and electronics engineering (ICE3) 2020 Feb 14 (pp. 452-457). IEEE.
- Radwan M, Mohamed Abdelrahman N, Wael Kamal H, Khaled Abdelmonem Elewa A, Moataz Mohamed A. MLHeartDisPrediction: heart disease prediction using machine learning. Journal of Computing and Communication. 2023 Jan 25;2(1):50-65.
- Gupta C, Saha A, Reddy NS, Acharya UD. Cardiac Disease Prediction using Supervised Machine Learning Techniques. InJournal of Physics: Conference Series 2022 (Vol. 2161, No. 1, p. 012013). IOP Publishing.
| Volume | 02 |
| Issue | 01 |
| Received | March 28, 2024 |
| Accepted | April 8, 2024 |
| Published | May 9, 2024 |