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Heart Disease Prediction Model Based On Herding Exploring Algorithm With Light Gradient Boosting
Abstract: This invention proposes a machine learning technique named Herding exploring algorithm-based Light Gradient Boosting Machine classifier (HEOA-based LightGBM) for the effective prediction of heart disease. The proposed HEOA is developed through hybridizing the herding characteristics of elephants and exploring characteristics of earthlings, which effectively tunes the hyper-parameters of LightGBM classifier. The experimental outcomes show that the proposed HEOA-based LightGBM provides the highest accuracy, sensitivity, and specificity of 93.064%, 95.618%, and 91.038% respectively.
Notices, Deadlines & Correspondence
Patent Information
Applicants
1. VIT-AP UNIVERSITY
Inventors
1. Girish S Bhavekar
2. Dr.Agam Das Goswami
Specification
1/We Claim:
1. A new prediction model based system for the fast and accurate detection of the heart disease in human beings has been proposed.
2. The system mentioned in the claim J, wherein The Herding-exploring algorithm (for heart disease prediction) which is selective swarm intelligence algorithm implemented by the hybridization of the herding characteristics and exploration characteristics of the standard EHO and SRO algorithms has
been utilized.
3. The system mentioned in the claim I, wherein the Herding-exploring algorithm for feature selection (For Heart Disease prediction) has been utilized wherein contemptible attributes are replaced by the informative attributes to reduce the computational complexity, which promotes the computational complexity of classification,
4. The system mentioned in the claim I, wherein the Proposed HEOA-based light BGM classifier for effective prediction of heart disease helps in acquiring informative attributes extracted from the feature selection are analysed and investigated
Documents
Application Documents
| # | Name | Date |
|---|---|---|
| 1 | 202241001418-Form9_Early Publication_11-01-2022.pdf | 2022-01-11 |
| 2 | 202241001418-Form5_As Filed_11-01-2022.pdf | 2022-01-11 |
| 3 | 202241001418-Form3_As Filed_11-01-2022.pdf | 2022-01-11 |
| 4 | 202241001418-Form28_Educational Institution_11-01-2022.pdf | 2022-01-11 |
| 5 | 202241001418-Form2 Title Page_Complete_11-01-2022.pdf | 2022-01-11 |
| 6 | 202241001418-Form1_As Filed_11-01-2022.pdf | 2022-01-11 |
| 7 | 202241001418-Form18_Examination Request_11-01-2022.pdf | 2022-01-11 |
| 8 | 202241001418-Drawings_As Filed_11-01-2022.pdf | 2022-01-11 |
| 9 | 202241001418-Description Complete_As Filed_11-01-2022.pdf | 2022-01-11 |
| 10 | 202241001418-Correspondence_Eligibility Document_11-01-2022.pdf | 2022-01-11 |
| 11 | 202241001418-Correspondence_As Filed_11-01-2022.pdf | 2022-01-11 |
| 12 | 202241001418-Claims_As Filed_11-01-2022.pdf | 2022-01-11 |
| 13 | 202241001418-Abstract_As Filed_11-01-2022.pdf | 2022-01-11 |
| 14 | 202241001418-FER.pdf | 2022-07-13 |
Search Strategy
| 1 | Search202241001418E_12-07-2022.pdf |