Intelligent Diagnosis of Obstetric Diseases Using HGS-AOA Based Extreme Learning Machine
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2023, Vol 2, Issue 1
Abstract
This paper aimed to realize intelligent diagnosis of obstetric diseases using electronic medical records (EMRs). The Optimized Kernel Extreme Machine Learning (OKEML) technique was proposed to rebalance data. The hybrid approach of the Hunger Games Search (HGS) and the Arithmetic Optimization Algorithm (AOA) was adopted. This paper tested the effectiveness of the OKEML-HGS-AOA on Chinese Obstetric EMR (COEMR) datasets. Compared with other models, the proposed model outperformed the state-of-the-art experimental results on the COEMR, Arxiv Academic Paper Dataset (AAPD), and the Reuters Corpus Volume 1 (RCV1) datasets, with an accuracy of 88%, 90%, and 91%, respectively.
Authors and Affiliations
Ramesh Vatambeti,Vijay Kumar Damera
Keywords
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- EP ID EP731881
- DOI https://doi.org/10.56578/ataiml020103
- Views 46
- Downloads 1
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Intelligent Diagnosis of Obstetric Diseases Using HGS-AOA Based Extreme Learning Machine
This paper aimed to realize intelligent diagnosis of obstetric diseases using electronic medical records (EMRs). The Optimized Kernel Extreme Machine Learning (OKEML) technique was proposed to rebalance data. The hybrid...