A hybrid machine learning approach to identify coronary diseases using feature selection mechanism on heart disease dataset
में बचाया:
| में प्रकाशित: | Distributed and Parallel Databases vol. 41, no. 1 (Jun 2023), p. 1 |
|---|---|
| मुख्य लेखक: | |
| अन्य लेखक: | , , |
| प्रकाशित: |
Springer Nature B.V.
|
| विषय: | |
| ऑनलाइन पहुंच: | Citation/Abstract Full Text Full Text - PDF |
| टैग: |
कोई टैग नहीं, इस रिकॉर्ड को टैग करने वाले पहले व्यक्ति बनें!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3255421161 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0926-8782 | ||
| 022 | |a 1573-7578 | ||
| 024 | 7 | |a 10.1007/s10619-021-07329-y |2 doi | |
| 035 | |a 3255421161 | ||
| 045 | 2 | |b d20230601 |b d20230630 | |
| 100 | 1 | |a Doppala, Bhanu Prakash |u Lincoln University College, Department of Computer Science and Multimedia, Petaling Jaya, Malaysia (GRID:grid.512179.9) (ISNI:0000 0004 1781 393X) | |
| 245 | 1 | |a A hybrid machine learning approach to identify coronary diseases using feature selection mechanism on heart disease dataset | |
| 260 | |b Springer Nature B.V. |c Jun 2023 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Coronary illness can be treated as one of the major causes for mortality globally. On-time and Precise conclusion on the type of disease is significant for therapy and breakdown expectancy. Research scientists are working rigorously in their respective fields to reduce the death rate. Even though lot of research took place on this area still there is a scope for increasing the prediction accuracy. The fundamental point of our proposed work is to build up a hybrid methodology using genetic algorithm (GA) with (RBF) radial basis function (GA-RBF) for the detection of coronary sickness with increased accuracy using the feature selection mechanism. The proposed system performance achieved an accuracy of 85.40% using 14 attributes, and the prediction accuracy increased to 94.20% with nine characteristics where the functionality of the proposed system performed much better after attribute reduction. | |
| 651 | 4 | |a India | |
| 653 | |a Machine learning | ||
| 653 | |a Asian people | ||
| 653 | |a Accuracy | ||
| 653 | |a Fatalities | ||
| 653 | |a Datasets | ||
| 653 | |a Regression analysis | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Radial basis function | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Cardiovascular disease | ||
| 653 | |a Researchers | ||
| 653 | |a Feature selection | ||
| 653 | |a Illnesses | ||
| 653 | |a Algorithms | ||
| 653 | |a Heart | ||
| 653 | |a Heart diseases | ||
| 700 | 1 | |a Bhattacharyya, Debnath |u K L Deemed to be University, KLEF, Department of Computer Science and Engineering, Guntur, India (GRID:grid.512179.9) | |
| 700 | 1 | |a Chakkravarthy, Midhun |u Lincoln University College, Department of Computer Science and Multimedia, Petaling Jaya, Malaysia (GRID:grid.512179.9) (ISNI:0000 0004 1781 393X) | |
| 700 | 1 | |a Kim, Tai-hoon |u Beijing Jiaotong University, School of Economics and Management, Beijing, China (GRID:grid.181531.f) (ISNI:0000 0004 1789 9622) | |
| 773 | 0 | |t Distributed and Parallel Databases |g vol. 41, no. 1 (Jun 2023), p. 1 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3255421161/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3255421161/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3255421161/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |