Heart disease prediction using ML through enhanced feature engineering with association and correlation analysis
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Heart disease prediction using ML through enhanced feature engineering with association and correlation analysis |
| 2. | Creator | Author's name, affiliation, country | Annemneedi Lakshmanarao; Aditya Engineering College; India |
| 2. | Creator | Author's name, affiliation, country | Thotakura Venkata Sai Krishna; QIS College of Engineering and Technology (Autonomous); India |
| 2. | Creator | Author's name, affiliation, country | Tummala Srinivasa Ravi Kiran; P.B. Siddhartha College of Arts and Science Vijayawada; India |
| 2. | Creator | Author's name, affiliation, country | Chinta Venkata Murali krishna; NRI Institute of Technology; India |
| 2. | Creator | Author's name, affiliation, country | Samsani Ushanag; University College of Engineering Kakinada; India |
| 2. | Creator | Author's name, affiliation, country | Nandikolla Supriya; Malla Reddy University; India |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Association analysis; Correlation analysis; Feature engineering; Heart disease; Kaggle; Machine learning |
| 4. | Description | Abstract | Heart disease remains a prevalent and critical health concern globally. This paper addresses the critical task of heart disease prediction through the utilization of advanced machine learning techniques. Our approach focuses on the enhancement of feature engineering by incorporating a novel integration of association and correlation analyses. A heart disease dataset from Kaggle was used for the experiments. Association analysis was applied to the categorical and binary features in the dataset. Correlation analysis was applied to the numerical features in the dataset. Based on the insights from association analysis and correlation analysis, a new dataset was created with combinations of features. Later, newly created features are integrated with the original dataset, and classification algorithms are applied. Five machine learning (ML) classifiers, namely decision tree, k-nearest neighbors (KNN), random forest, XG-Boost, and support vector machine (SVM), were applied to the final dataset and achieved a good accuracy rate for heart disease detection. By systematically exploring associations and relationships with categorical, binary, and numerical features, this paper unveils innovative insights that contribute to a more comprehensive understanding of the heart disease dataset. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2024-05-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/36137 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijeecs.v34.i2.pp1122-1130 |
| 11. | Source | Title; vol., no. (year) | Indonesian Journal of Electrical Engineering and Computer Science; Vol 34, No 2: May 2024 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2024 Institute of Advanced Engineering and Science This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |